Train positioning method based on fusion of 5g and electronic track map under railway 5g-r scenario

Abstract

This invention discloses a train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario. The method includes: transforming the track point set in the electronic track map from a geodetic coordinate system to a geocentric coordinate system to obtain a trajectory continuity function; determining the Time of Arrival (TOA) for each base station based on downlink PRS signals transmitted by multiple 5G base stations and a pre-configured local reference PRS signal; obtaining distance difference observations based on the Time Difference of Arrival (TDOA) between TOAs; estimating the Angle of Arrival (AOA) observation based on uplink SRS signals; constructing an objective function for TDOA based on the trajectory continuity function and the distance difference observation, and pruning the feasible solution region using the AOA observation; and solving the objective function within the pruned feasible solution region using a coarse search and locally bounded optimization algorithm to obtain a train position estimate. This invention can still obtain stable and accurate positioning results even when there are insufficient available positioning base stations.

Description

Technical Field

[0001] This invention relates to the field of train positioning and wireless positioning technology, specifically to a train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario. Background Technology

[0002] Train control systems play a crucial role in railway transportation systems, and location information is one of the key pieces of information during train operation. With the rapid development of 5G technology, its application in railway transportation has gradually become a research hotspot. In the interim standard for the 5G Railway (5G-R) network, based on 5G technology, base stations are deployed at 1500m intervals.

[0003] However, due to the limited coverage and layout of base station signals, areas along railway lines cannot obtain sufficient base station support for actual positioning applications. Summary of the Invention

[0004] This invention provides a train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario, so as to obtain stable and accurate positioning results even when there are insufficient traditional available positioning base stations.

[0005] This invention provides a train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario. The method includes:

[0006] Obtain an electronic orbit map and transform the orbit point set in the electronic orbit map from the geodetic coordinate system to the geocentric coordinate system to obtain the trajectory continuity function of the orbit point set;

[0007] The train user terminal receives downlink PRS signals from multiple 5G base stations and determines the arrival time (TOA) for each base station based on the downlink PRS signals and the local reference PRS signals pre-configured by the base stations; the distance difference observation value is obtained based on the arrival time difference (TDOA) between the arrival times (TOAs).

[0008] The base station receives the uplink SRS signal sent by the train user terminal and estimates the angle of arrival (AOA) observation based on the uplink SRS signal.

[0009] Based on the trajectory continuity function and distance difference observations, the objective function of TDOA is constructed in the one-dimensional domain of the orbit, and the feasible solution region is clipped using the angle of arrival (AOA) observations.

[0010] Within the trimmed feasible solution region, the objective function is solved using coarse search and local bounded optimization algorithms to obtain the estimated train position.

[0011] In some embodiments of the present invention, within the trimmed feasible solution region, a coarse search and local bounded optimization algorithm is used to solve the objective function to obtain the train position estimate, including:

[0012] Throughout the entire orbit, the objective function is discretely sampled with a preset step size, and sampling points that do not conform to the angle measurement are eliminated using the angle of arrival (AOA) observation to obtain a set of valid candidate points.

[0013] Select the sampling point that minimizes the objective function from the set of valid candidate points, and use it as the initial solution for the coarse search;

[0014] A local search window is constructed with the initial solution of the coarse search as the center. Within the local search window, the objective function is solved using a one-dimensional bounded nonlinear minimization algorithm to obtain the estimated train position.

[0015] In some embodiments of the present invention, the objective function J(s) is characterized as:

[0016] ;

[0017] (s)=‖p(s-b A ||-||p(s)-b B ||;

[0018] In the formula, s is the orbital position parameter, p(s) is the trajectory continuity function, and b A b B These are the location information of base station A and base station B in the geocentric coordinate system, respectively. This represents the observed distance difference between base station A and base station B.

[0019] In some embodiments of the present invention, the Time of Arrival (TOA) for each base station is determined based on the downlink PRS signal and the local reference PRS signal pre-configured by the base station; the distance difference observation is obtained based on the Time of Arrival Difference (TDOA) between the TOAs, including:

[0020] A sliding cross-correlation operation is performed on the downlink PRS signal and the local reference PRS signal to obtain the correlation amplitude sequence;

[0021] Peak detection is performed on the relevant amplitude sequence to determine the peak position corresponding to the first arrival path, and the peak position is mapped to the time of arrival (TOA).

[0022] The initial distance difference observation is obtained based on the time difference of arrival (TDOA) between any two base stations (TOA).

[0023] Based on the TDOA measurements, a robust estimation algorithm is used to estimate the system bias, and the bias estimate is used to calibrate the initial distance difference observations to obtain the final distance difference observations.

[0024] In some embodiments of the present invention, based on TDOA measurements, a robust estimation algorithm is used to estimate system bias, and the estimated bias values ​​are used to calibrate the initial distance difference observations to obtain the final distance difference observations, including:

[0025] The theoretical time difference prediction is calculated using the previous epoch or candidate epoch, and a residual sequence is constructed based on the TDOA measurement and the theoretical time difference prediction.

[0026] Perform systematic bias estimation on the residual sequence to obtain the bias estimate;

[0027] The time difference of arrival (TDOA) is calibrated using the deviation estimate, and the final distance difference observation is obtained based on the calibrated time difference.

[0028] In some embodiments of the present invention, the base station receives the uplink SRS signal sent by the train user terminal and estimates the angle of arrival (AOA) observation based on the uplink SRS signal, including:

[0029] The base station recovers the uplink reference signal SRS according to the SRS configuration parameters, and uses the uplink reference signal SRS to perform channel estimation on the uplink SRS signal to obtain channel phase information;

[0030] Based on channel phase information, in the m-th epoch of continuous measurement, a scanable receiving beam is formed by controlling the phase shift and amplitude weight of each channel of the antenna array. The incident direction is determined according to the power of the uplink SRS signal under different beam directions, and the angle of arrival (AOA) observation is obtained by interpolation.

[0031] In some embodiments of the present invention, the SRS configuration parameters include a time domain start position of 13, a period configuration of 20 time slots, a frequency domain start physical resource block position of 0, an SRS bandwidth configuration of 48 physical resource blocks, a comb size of 4, a transmission comb offset of 0, and a cyclic shift of 0.

[0032] By controlling the phase shift and amplitude weight of each channel of the antenna array, a scannable receiving beam is formed. The incident direction is determined based on the power of the uplink SRS signal under different beam directions. Then, the angle of arrival (AOA) observation is obtained through interpolation, including:

[0033] The base station antenna array pre-calculates a set of DFT beam weights covering the range of [-60°, 60°], with a step of 1°;

[0034] For each SRS reception time, the uplink SRS signal and each DFT beam weight are weighted and combined, and the power of the combined signal is calculated. The beam direction with the highest power is selected as the AOA coarse estimate.

[0035] Based on the coarse AOA estimate, parabolic interpolation is performed on the power of a preset number of beams near the peak to obtain the AOA observation.

[0036] In some embodiments of the present invention, the feasible solution region is clipped using the angle of arrival (AOA) observation, including:

[0037] Transform the orbit point set to the ENU coordinate system with the base station as the origin, and calculate the predicted azimuth angle of the orbit point set in the ENU coordinate system relative to the base station.

[0038] If the absolute value of the difference between the predicted azimuth and the AOA observation is greater than the preset threshold, a maximum penalty value is assigned to the orbital point set when calculating the objective function, or the orbital point set is directly excluded from the candidate set.

[0039] In some embodiments of the present invention, the trajectory point set in the electronic trajectory map is transformed from a geodetic coordinate system to a geocentric coordinate system to obtain a trajectory continuity function of the trajectory point set, including:

[0040] Based on the WGS-84 ellipsoid parameters, the track point set in the electronic track map is transformed from the geodetic coordinate system to the geocentric geofixed coordinate system, and the track point set, base station coordinates and train position are placed in the same three-dimensional coordinate system through a unified coordinate datum.

[0041] Construct track position parameters from the Mileage in the electronic track map;

[0042] Piecewise linear interpolation, cubic spline interpolation, or piecewise polynomial interpolation are used to process the orbital position parameters to construct a continuous trajectory function.

[0043] In the train positioning method based on the fusion of 5G and electronic track maps in the railway 5G-R scenario provided by this invention, the train user terminal receives downlink PRS signals sent by multiple 5G base stations, and determines the Time of Arrival (TOA) corresponding to each base station based on the downlink PRS signals and the local reference PRS signals pre-configured by the base stations; the distance difference observation is obtained based on the Time Difference of Arrival (TDOA) between the TOAs, thereby obtaining the distance difference constraint; the base station receives uplink SRS signals sent by the train user terminal, and estimates the Angle of Arrival (AOA) observation based on the uplink SRS signals, thereby obtaining the directional approximation. Based on the trajectory continuity function and distance difference observations, a TDOA objective function is constructed in the one-dimensional domain of the track, and the feasible solution region is clipped using the angle of arrival (AOA) observations. Within the clipped feasible solution region, a coarse search and local bounded optimization algorithm is used to solve the objective function to obtain the estimated train position. This allows the track geometry prior provided by the electronic track map to be introduced into the solution process, reducing the three-dimensional spatial positioning problem to a one-dimensional optimization problem along the track arc length. This improves the positioning accuracy and continuity under conditions with few base stations, enabling stable and accurate positioning results to be obtained even when traditional available positioning base stations are insufficient. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used 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 those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario provided by an embodiment of the present invention.

[0046] Figure 2 This is a schematic diagram of a railway 5G-R scenario provided in an embodiment of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0049] "A and / or B" includes the following three combinations: A only, B only, and a combination of A and B.

[0050] The use of "applies to" or "configured to" in this invention implies an open and inclusive language, which does not exclude the applicability to or configuration to devices performing additional tasks or steps. Additionally, the use of "based on" implies openness and inclusivity, because processes, steps, calculations, or other actions "based on" one or more conditions or values ​​may in practice be based on additional conditions or values ​​beyond those conditions.

[0051] In this invention, the term "exemplary" is used to mean "serving as an example, illustration, or description." Any embodiment described as "exemplary" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0052] The following describes, with reference to the accompanying drawings, a train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario provided by an embodiment of the present invention.

[0053] like Figure 1 As shown, this embodiment of the invention provides a train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario. The method includes the following steps:

[0054] S101, acquire the Digital Track Map (DTM), and transform the track point set in the digital track map from the geodetic coordinate system to the geocentric coordinate system to obtain the trajectory continuity function of the track point set.

[0055] S102 receives downlink PRS (Positioning Reference Signal, PRS) signals from multiple 5G base stations through the train user terminal, and determines the time of arrival (TOA) (Time Difference of Arrival, TDOA) for each base station based on the downlink PRS signal and the local reference PRS signal pre-configured by the base station; and obtains the distance difference observation value based on the time difference of arrival (TDOA) between the arrival times (TOAs).

[0056] S103, the base station receives the uplink SRS (Sounding Reference Signal) signal sent by the train user terminal, and estimates the angle of arrival (AOA) observation based on the uplink SRS signal.

[0057] S104, based on the trajectory continuity function and distance difference observations, constructs the objective function of TDOA in the one-dimensional domain of the orbit and uses the angle of arrival (AOA) observations to prune the feasible solution region.

[0058] Indicatively, as Figure 2 Taking base stations A and B as examples, the green line represents the uplink SRS signal sent by the train user terminal. The 5G base station can calculate the AOA by receiving the SRS, and the user can calculate the TDOA hypercurve by receiving the PRS. The intersection of the hypercurve and the DTM in the figure is the train position by modeling and solving the objective function.

[0059] S105. Within the trimmed feasible solution region, the objective function is solved using a coarse search and local bounded optimization algorithm to obtain the estimated train position.

[0060] In the train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario provided by this invention, the train user terminal receives downlink PRS signals sent by multiple 5G base stations, and determines the Time of Arrival (TOA) corresponding to each base station based on the downlink PRS signals and the local reference PRS signals pre-configured by the base stations; the distance difference observation is obtained based on the Time Difference of Arrival (TDOA) between the TOAs, thereby obtaining the distance difference constraint; the base station receives uplink SRS signals sent by the train user terminal, and estimates the Angle of Arrival (AOA) observation based on the uplink SRS signals, thereby obtaining the direction. Constraints: Based on the trajectory continuity function and distance difference observations, a TDOA objective function is constructed in the one-dimensional domain of the track, and the feasible solution region is clipped using the angle of arrival (AOA) observations. Within the clipped feasible solution region, a coarse search and local bounded optimization algorithm is used to solve the objective function to obtain the estimated train position. This allows the track geometry prior provided by the electronic track map to be introduced into the solution process, reducing the three-dimensional spatial positioning problem to a one-dimensional optimization problem along the track arc length. This improves the positioning accuracy and continuity under conditions with few base stations, enabling stable and accurate positioning results to be obtained even when traditional available positioning base stations are insufficient.

[0061] In some embodiments of the present invention, step S105 includes the following sub-steps:

[0062] S1051, within the entire orbital range [0, L], the objective function J(s) is discretely sampled with a preset step size ΔS, and sampling points that do not conform to the angle measurement are eliminated using the angle of arrival (AOA) observation to obtain a set of valid candidate points.

[0063] S1052, select the sampling point s0 that minimizes the objective function from the set of valid candidate points, and use it as the initial solution for the coarse search. (Illustratively,) .

[0064] S1053, construct a local search window [max(0, s0-W), min(L, s0+W)] centered on the initial solution of the coarse search, where W is the half-width of the window; within the local search window, solve the objective function J(s) using a one-dimensional bounded nonlinear minimization algorithm to obtain the optimal parameters. Then output the estimated train position. Indicatively, ; .

[0065] In some examples, when obtaining train position estimates Afterwards, residual calculations were performed:

[0066] ;

[0067] In the formula, The degree of fit between the estimated train position and the observed values. Based on optimal parameters The TDOA distance difference model, These are the observed distance differences between base station A and base station B. The smaller the value, the more consistent the estimated location is with the measured observation, and the higher the reliability of the positioning result.

[0068] In some embodiments of the present invention, the objective function J(s) is characterized as:

[0069] ;

[0070] (s)=‖p(s-b A ||-||p(s)-b B ||;

[0071] In the formula, s is the orbital position parameter, p(s) is the trajectory continuity function, and b A b B These are the location information of base station A and base station B in the geocentric coordinate system, respectively. This represents the observed distance difference between base station A and base station B.

[0072] In some embodiments of the present invention, step S102 includes the following sub-steps:

[0073] S1021, Perform sliding cross-correlation operation on the downlink PRS signal and the local reference PRS signal to obtain the correlation amplitude sequence.

[0074] In some examples, the sliding cross-correlation operation is implemented using a sliding window function of the received signal and the local reference PRS, which is set as follows:

[0075] ;

[0076] In the formula, R(τ) is the sliding cross-correlation value corresponding to the candidate delay τ; τ is the sliding delay of the local reference PRS signal relative to the received PRS signal; k is the PRS symbol index, k=1,2,…,12; m is the index of the discrete sampling points participating in the correlation operation within the k-th PRS symbol; N is the number of sampling points participating in the correlation operation within each PRS symbol; r received (m,k) represents the complex baseband signal value received by the receiver at the k-th PRS symbol and the m-th sampling point; r template (m,k+τ) represents the complex baseband signal value at the corresponding position after the local reference PRS signal is shifted by τ sampling points.

[0077] The output of the sliding window function described above forms a correlation amplitude sequence that varies with the sampling points.

[0078] S1022, perform peak detection on the relevant amplitude sequence, determine the peak position corresponding to the first arrival path, and map the peak position to the arrival time (TOA).

[0079] S1023, based on the time difference of arrival (TDOA) between the arrival times (TOA) of any two base stations, obtain the initial distance difference observation value. Schematic, the time difference of arrival (Δt) AB =τ A -τ B , τ A Let TOA be the arrival time of base station A, and τ be the arrival time of base station A. B Let TOA be the arrival time of base station B, and Δt be the arrival time of base station B. AB The initial time difference of arrival between base station A and base station B; the initial distance difference observation value Δ AB =c·Δt AB c is the speed of light.

[0080] S1024. Based on the TDOA measurement, a robust estimation algorithm is used to estimate the system bias, and the initial distance difference observation is calibrated using the bias estimate to obtain the final distance difference observation.

[0081] In some examples, TDOA measurements ;Δd(s k ) represents the orbital parameter s k The corresponding geometric distance difference between the two base stations, where c is the speed of light; b(k) is a slowly varying deviation term used to characterize the systematic errors introduced by multipath propagation noise, etc. This represents random noise.

[0082] In some embodiments of the present invention, step S102 further includes:

[0083] S1020, configures and generates local reference PRS signals for n 5G base stations.

[0084] In some examples, for n 5G base stations, carrier parameters are configured, including a carrier center frequency of 2.1 GHz, a subcarrier spacing of 15 kHz, a resource grid size of 52 (corresponding to approximately 9.36 MHz bandwidth), and a physical cell identifier. PRS parameters are configured, including a PRS identifier, a PRS resource set period [4 0], a PRS slot offset (0, 1 to distinguish different base stations), a comb size of 4, 12 PRS symbols per slot, a resource block offset of 0, and a starting symbol index of 0.

[0085] To reduce the impact of PRS cross-correlation between different base stations, different PRS time slot offsets and different PRS identifier configurations are adopted for different base stations. OFDM modulation is used to ensure that the PRS resources of each base station can be distinguished in the time-frequency domain and frequency domain under the same carrier.

[0086] In some embodiments of the present invention, in order to suppress slow-varying biases and random noise caused by multipath propagation noise, a sliding window stationary estimation is performed on the Time of Arrival (TOA) sequence obtained by PRS correlation. Specifically, step S1024 includes the following sub-steps:

[0087] S201, Calculate the theoretical time difference prediction value using the previous epoch position or candidate position. Based on TDOA measurements and theoretical time difference predictions, residual sequences were constructed. .

[0088] Indicatively, , In the formula, δt pred (k) represents the theoretical arrival time difference prediction value corresponding to the kth epoch; These are the orbital position parameters for the previous epoch k-1; For the train's position on the track The difference in geometric distance between the two base stations relative to time; c is the speed of light; r i Let δt be the TDOA residual of the i-th epoch; meas (i) is the TDOA measurement value obtained by PRS correlation peak detection in the i-th epoch; is the theoretical arrival time difference prediction value corresponding to the i-th epoch; i is the epoch index within the sliding window; N is the length of the sliding window.

[0089] S202, perform systematic bias estimation on the residual sequence to obtain the bias estimate value.

[0090] In some examples, the median is used to estimate the systematic bias based on the residual sequence, thus obtaining the bias estimate. : .

[0091] In some other examples, where a smoother output is required, Huber M estimation is used to robustly weight the ranging error caused by multipath propagation during the positioning process, suppressing abnormal base station observations. The bias estimate is obtained by iteratively solving the weighted mean. :

[0092] ;

[0093] In the formula, The system bias variable to be estimated is... It is the optimal system deviation estimate.

[0094] S203, use the deviation estimate to calibrate the time difference of arrival (TDOA), and obtain the final distance difference observation value based on the calibrated time difference.

[0095] In some examples, the calibrated time difference ,or In the formula, The arrival time difference after calibration at the k-th epoch; This represents the original arrival time difference measurement at the k-th epoch; k is the measurement epoch index. This is the estimated value of the systematic bias in the k-th epoch; This is the optimal system bias estimate obtained through a robust estimation algorithm.

[0096] In some embodiments of the present invention, step S103 includes the following sub-steps:

[0097] S1031, the base station recovers the uplink reference signal SRS according to the SRS configuration parameters, and uses the uplink reference signal SRS to perform channel estimation on the uplink SRS signal to obtain the channel phase information of each element of the antenna array.

[0098] S1032, based on channel phase information, in the m-th epoch of continuous measurement, forms a scanable receiving beam by controlling the phase shift and amplitude weight of each channel of the antenna array, and determines the incident direction according to the power of the uplink SRS signal under different beam directions, and then obtains the angle of arrival (AOA) observation by interpolation.

[0099] In some embodiments of the present invention, the SRS configuration parameters include a time domain start position of 13, a period configuration of 20 time slots, a frequency domain start physical resource block position of 0, an SRS bandwidth configuration of 48 physical resource blocks, a comb size of 4, a transmission comb offset of 0, and a cyclic shift of 0.

[0100] Understandably, the specific configuration parameters of the SRS are designed to ensure orthogonality with the time-frequency resources of the PRS. To avoid conflicts, the SRS is configured in a time slot with a time slot offset of 4, and the last symbol in the time slot is used to achieve complete time-division multiplexing. In the frequency domain, the SRS bandwidth is 48 PRBs, and the comb size is 4, consistent with the PRS, but the transmission comb offset is set to 0 to ensure that the REs of the SRS do not overlap with those of the PRS.

[0101] By controlling the phase shift and amplitude weight of each channel of the antenna array, a scannable receiving beam is formed. The incident direction is determined based on the power of the uplink SRS signal under different beam directions. Then, the angle of arrival (AOA) observation is obtained through interpolation, including:

[0102] The base station antenna array pre-calculates a set of DFT beam weights covering the range of [-60°, 60°], with a step of 1°;

[0103] For each SRS reception time, the uplink SRS signal and each DFT beam weight are weighted and combined, and the power of the combined signal is calculated. The beam direction with the highest power is selected as the AOA coarse estimate.

[0104] Based on the coarse AOA estimate, parabolic interpolation is performed on the power of a preset number of beams (e.g., 3) near the peak to obtain the AOA observation θ. i .

[0105] Understandably, the current train control system uses a multi-source fusion approach to determine the train's direction of travel, with the AOA estimate θ obtained from continuous observations from the same base station. i The range of change can determine whether the train is currently approaching or moving away from the base station, and can therefore serve as one of the information sources for the train control system to determine the direction of train operation.

[0106] In some embodiments of the present invention, the feasible solution region is clipped using the angle of arrival (AOA) observation, including:

[0107] Transform the orbit point set to the ENU coordinate system with base station A as the origin, and calculate the predicted azimuth angle of the orbit point set relative to the base station in the ENU coordinate system.

[0108] In some examples, the azimuth of candidate points on the track is first checked using angle-of-arrival (AOA) observations. Candidate points p(s) that meet the azimuth requirements are then projected onto the ENU plane, and their predicted azimuth relative to the base station is calculated. Schematic, base station b... i Predicted azimuth In the formula, p(s) represents the predicted azimuth angle corresponding to the i-th base station; p(s) represents the candidate orbit point corresponding to the trajectory continuity function at the orbit position parameter s; b i Let i be the location of the i-th base station; The function is the arctangent function in the four quadrants; x(s) and y(s) are the x and y coordinates of the candidate orbital point p(s) projected onto the ENU plane, respectively; x i and y i For the i-th base station b i x and y coordinates in the ENU plane.

[0109] If the absolute value of the difference between the predicted azimuth and the AOA observation is greater than the preset threshold, a maximum penalty value is assigned to the orbital point set when calculating the objective function, or the orbital point set is directly excluded from the candidate set.

[0110] In some embodiments of the present invention, step S101 includes the following sub-steps:

[0111] S1011, based on WGS-84 ellipsoid parameters, transforms the track point set in the electronic track map from the geodetic coordinate system to the geocentric coordinate system, and uses a unified coordinate datum to place the track point set, base station coordinates and train position in the same three-dimensional coordinate system.

[0112] The DTM data includes information such as track tree identifier, track identifier, track name, track type, track start and end distance, track start and end latitude and longitude, and track point sequence data.

[0113] S1012, construct the orbital position parameters from the Mileage in the electronic orbital map. In some examples, the orbital position parameters s can be directly obtained from the Mileage mapping, and a strictly monotonic sequence is checked.

[0114] S1013 uses piecewise linear interpolation, cubic spline interpolation, or piecewise polynomial interpolation to process the orbital position parameters and construct a continuous trajectory function.

[0115] In some examples, the electronic track map (DTM) data is taken as input and parsed into a track point set. The track point set contains track ID, longitude (Lon), latitude (Lat), elevation (Hgt), and mileage. The track point set is then filtered by TrackID, the field integrity is checked, outliers are removed, and the data is sorted to obtain a preprocessed track point set.

[0116] The preprocessed orbit point set is transformed from geodetic coordinates (Lon, Lat, Hgt) to the geocentric Earth-fixed coordinate system ECEF.

[0117] Using the Mileage field in the electronic track map data, the track position parameter s is constructed and verified by incremental sorting; then, the trajectory continuity function p(s) = [x(s), y(s), z(s)] is constructed to determine the three-dimensional position of the train in the ECEF coordinate system on the track.

[0118] Trajectory Continuity Function Obtained using piecewise linear interpolation:

[0119] ;

[0120] In the formula, p(s) is the three-dimensional position vector corresponding to the trajectory continuous function at the orbital position parameter s; s j and s j+1 These are the orbital position parameters corresponding to the j-th orbital point and the (j+1)-th orbital point, respectively; p j and p j+1 These are the three-dimensional position vectors of the j-th and (j+1)-th orbital points in a unified coordinate system.

[0121] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0122] The above provides a detailed description of a train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario, as provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario, characterized in that, The method includes: Obtain an electronic orbit map and transform the orbit point set in the electronic orbit map from the geodetic coordinate system to the geocentric coordinate system to obtain the trajectory continuity function of the orbit point set; The train user terminal receives downlink PRS signals from multiple 5G base stations, and determines the arrival time (TOA) of each base station based on the downlink PRS signals and the local reference PRS signals pre-configured by the base stations; and obtains the distance difference observation value based on the arrival time difference (TDOA) between the arrival times (TOAs). The base station receives the uplink SRS signal sent by the train user terminal and estimates the angle of arrival (AOA) observation based on the uplink SRS signal. Based on the trajectory continuity function and distance difference observations, the objective function of TDOA is constructed in the one-dimensional domain of the orbit, and the feasible solution region is pruned using the angle of arrival (AOA) observations. Within the trimmed feasible solution region, the objective function is solved using a coarse search and local bounded optimization algorithm to obtain the estimated train position.

2. The train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario according to claim 1, characterized in that, Within the trimmed feasible solution region, the objective function is solved using a coarse search and local bounded optimization algorithm to obtain the train position estimate, including: Within the entire orbital range, the objective function is discretely sampled with a preset step size, and sampling points that do not conform to the angle measurement are eliminated using the angle of arrival (AOA) observation to obtain a set of valid candidate points; Select the sampling point that minimizes the objective function from the set of valid candidate points, and use it as the initial solution for the coarse search; A local search window is constructed centered on the initial coarse search solution. Within the local search window, the objective function is solved using a one-dimensional bounded nonlinear minimization algorithm to obtain the estimated train position.

3. The train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario according to claim 1, characterized in that, The objective function J(s) is characterized as follows: ； (s)=‖p(s)-b A ‖-‖p(s)-b B ‖; In the formula, s is the orbital position parameter, p(s) is the trajectory continuity function, and b A b B These are the location information of base station A and base station B in the geocentric coordinate system, respectively. This represents the observed distance difference between base station A and base station B.

4. The train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario according to claim 1, characterized in that, The arrival time (TOA) of each base station is determined based on the downlink PRS signal and the local reference PRS signal pre-configured by the base station. Based on the time difference of arrival (TDOA) between arrival times (TOA), distance difference observations are obtained, including: Perform a sliding cross-correlation operation on the downlink PRS signal and the local reference PRS signal to obtain the correlation amplitude sequence; Peak detection is performed on the relevant amplitude sequence to determine the peak position corresponding to the first arrival path, and the peak position is mapped to the arrival time (TOA). The initial distance difference observation is obtained based on the time difference of arrival (TDOA) between any two base stations (TOA). Based on the TDOA measurement, a robust estimation algorithm is used to estimate the system bias, and the bias estimate is used to calibrate the initial distance difference observation to obtain the final distance difference observation.

5. The train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario according to claim 4, characterized in that, The process of estimating system bias based on TDOA measurements using a robust estimation algorithm, and then calibrating the initial distance difference observations using the bias estimate to obtain the final distance difference observations, includes: The theoretical time difference prediction is calculated using the previous epoch or candidate epoch, and a residual sequence is constructed based on the TDOA measurement and the theoretical time difference prediction. The residual sequence is subjected to systematic bias estimation to obtain the bias estimate value; The time difference of arrival (TDOA) is calibrated using the deviation estimate, and the final distance difference observation is obtained based on the calibrated time difference.

6. The train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario according to claim 1, characterized in that, The base station receives the uplink SRS signal sent by the train user terminal and estimates the angle of arrival (AOA) observation based on the uplink SRS signal, including: The base station recovers the uplink reference signal SRS according to the SRS configuration parameters, and uses the uplink reference signal SRS to perform channel estimation on the uplink SRS signal to obtain channel phase information; Based on the channel phase information, in the m-th epoch of continuous measurement, a scanable receiving beam is formed by controlling the phase shift and amplitude weight of each channel of the antenna array, and the incident direction is determined according to the power of the uplink SRS signal under different beam directions. Then, the angle of arrival (AOA) observation is obtained by interpolation.

7. The train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario according to claim 6, characterized in that, The SRS configuration parameters include a time domain start position of 13, a period configuration of 20 time slots, a frequency domain start physical resource block position of 0, an SRS bandwidth configuration of 48 physical resource blocks, a comb size of 4, a transmission comb offset of 0, and a cyclic shift of 0. The process involves controlling the phase shift and amplitude weights of each channel of the antenna array to form a scannable receiving beam, determining the incident direction based on the power of the uplink SRS signal under different beam directions, and then obtaining the angle of arrival (AOA) observation through interpolation. This includes: The base station antenna array pre-calculates a set of DFT beam weights covering the range of [-60°, 60°], with a step of 1°; For each SRS reception time, the uplink SRS signal and each DFT beam weight are weighted and combined, and the power of the combined signal is calculated. The beam direction with the highest power is selected as the AOA coarse estimate. Based on the coarse AOA estimation, parabolic interpolation is performed on the power of a preset number of beams near the peak to obtain the AOA observation.

8. The train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario according to claim 1, characterized in that, The process of using the angle of arrival (AOA) observation to clip the feasible solution region includes: Transform the orbit point set to the ENU coordinate system with the base station as the origin, and calculate the predicted azimuth angle of the orbit point set in the ENU coordinate system relative to the base station. If the absolute value of the difference between the predicted azimuth and the AOA observation is greater than the preset threshold, a maximum penalty value is assigned to the orbital point set when calculating the objective function, or the orbital point set is directly excluded from the candidate set.

9. The train positioning method based on the fusion of 5G and electronic track maps in a railway 5G-R scenario according to any one of claims 1 to 8, characterized in that, The step of transforming the orbit point set in the electronic orbit map from the geodetic coordinate system to the geocentric coordinate system to obtain the trajectory continuity function of the orbit point set includes: Based on the WGS-84 ellipsoid parameters, the track point set in the electronic track map is transformed from the geodetic coordinate system to the geocentric geofixed coordinate system, and the track point set, base station coordinates and train position are placed in the same three-dimensional coordinate system through a unified coordinate reference. The track position parameters are constructed using the Mileage in the electronic track map; The trajectory position parameters are processed using piecewise linear interpolation, cubic spline interpolation, or piecewise polynomial interpolation to construct the trajectory continuity function.

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