Train positioning method based on ultra-wideband enhanced Beidou and virtual transponder

Abstract

The invention provides a train positioning method based on an ultra wide band enhanced Beidou and a virtual transponder. The method comprises the following steps: establishing a virtual transponder basic database and an ultra-wideband equipment basic database according to collected data of a target track line; establishing a positioning observation quality basic database according to the Beidou positioning observation quality of the target track line, and compiling an ultra-wideband / Beidou combined positioning strategy set; in the train operation process, a positioning strategy is dynamically selected according to Beidou signal real-time observation conditions, the three-dimensional space position of current operation of the train is calculated, an ultra-wideband positioning calculation data source of the train is obtained, and traveled distance information of the train at the next moment is calculated. And determining the travelled distance of the train in the next positioning calculation period and the pre-capture triggering moment, and sending position correction information at the capture moment of the nearest target virtual transponder to be captured. According to the method, the ultra-wideband technology and Beidou positioning are combined, and the capturing precision and stability of the virtual transponder in a complex environment are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit train operation control, and in particular to a train positioning method based on ultra-wideband enhanced Beidou and a virtual transponder. Background Art

[0002] The virtual transponder uses Beidou positioning terminals and other equipment to obtain information such as the real-time position and speed of the train. By comparing the train position calculated by the current Beidou positioning with the preset virtual reference position, the exact time when the train passes the reference position is determined. When the train approaches the real transponder position, the on-board equipment triggers the internal logic, uses the interface and information structure consistent with the real transponder, reports the accurate position of the train and performs the transponder message transmission function. In this process, ensuring the continuous and accurate acquisition of the real-time position of the train, and on this basis, seamlessly and accurately determining whether the train passes the preset reference position is the key condition for realizing the timely triggering of the virtual transponder function and ensuring the normal operation of the train. To this end, the seamless capture of the virtual transponder requires the system to provide stable, accurate and reliable virtual transponder capture services under various environmental conditions, avoid missed capture and maintain high capture accuracy. Seamless capture can be divided into two levels: at the positioning level, the system guarantees that reliable and available positioning information can be provided under any circumstances to avoid positioning failure interruptions due to changes in the quality of Beidou signal observations; at the capture level, the system avoids possible missed capture problems with the highest possible capture accuracy.

[0003] Ultra-wideband (UWB) technology is valued for its high-precision positioning capability in complex environments. As an effective regional non-Beidou positioning method, it can locate multiple types of elements other than trains in the deployment area, such as personnel positioning. There have been attempts to implement train and personnel positioning based on ultra-wideband technology on rail transit lines such as the Beijing Metro. Many smart stations and smart construction sites have introduced ultra-wideband to provide regional positioning for equipment and personnel, which have been reflected and verified in actual environments. The advantages and value of ultra-wideband positioning technology in the railway system. Taking into account the core characteristics and main advantages of ultra-wideband positioning technology, as well as its significant low-cost characteristics compared to Beidou alternative / enhancement solutions such as Beidou pseudo-satellites, it has great development and application potential as an effective Beidou positioning enhancement method for specific regions. For traditional Beidou-based virtual transponder solutions, the introduction of the regional enhancement mechanism provided by ultra-wideband positioning under special conditions such as limited Beidou services and degraded Beidou positioning performance will effectively ensure that the implementation of virtual transponder technology has a continuous and reliable source of basic information, and thus provide support for the seamless optimization of virtual transponder functions.

[0004] The existing technology focuses on the typical scenarios in the railway environment and studies the precise single-point positioning method of trains under the conditions of limited satellite signals. For the railway environment, on-board sensors such as INS and ODO are selected to build a satellite augmentation system, and accurate GNSS / INS and INS / ODO mathematical models and error equations are established. The GNSS / INS / ODO combined positioning method is further constructed, and digital track maps are used to assist positioning to meet the continuous positioning requirements of trains in different environments.

[0005] A virtual balise capture scheme in the prior art includes operating based on a fixed capture radius (CaptureInterval, CI). The virtual balise detects the distance of the train from its real position within a radius R, with a preset virtual reference point as the center, and sends a corresponding balise message when it first appears within the capture circle.

[0006] Another virtual transponder capture scheme in the prior art includes using a GPS receiver to provide positioning information, using a fitting function to extrapolate the speed of several points when the GPS cannot provide positioning information, or reinitializing after receiving the GPS positioning information again, and using the current speed model to dynamically predict the speed at the current moment to determine whether the train is within the capture range of the virtual transponder at the current moment, thereby achieving the capture of the virtual transponder.

[0007] Another virtual balise capture scheme in the prior art uses an improved Sage-Husa adaptive filtering algorithm to reduce satellite positioning errors, provide accurate train positioning data for virtual balise capture, and adopts a composite map matching method. Different map matching algorithms are used according to the different characteristics of the railway lines. A data interpolation method based on the Kriging algorithm is used to increase sample points to reduce the risk of missed capture of virtual balises.

[0008] The disadvantages of the virtual transponder acquisition solution in the above prior art include:

[0009] (1) In the existing Beidou replacement / enhancement solutions, inertial sensors, odometers and other technologies all have the problem of error accumulation. In the case of reducing the application of physical transponders and Beidou being limited, there is a lack of information capabilities to accurately calibrate the accumulated errors;

[0010] (2) The existing virtual transponder capture solution is not environmentally adaptable and does not fully consider the switching operation of trains in different environments and the processing of complex factors that may affect Beidou positioning performance, such as interference with Beidou signals. It also lacks a dynamic compensation mechanism when Beidou signal performance degrades in complex operating environments.

[0011] (3) The capture accuracy of existing virtual transponders is limited. The use of a fixed capture radius makes it difficult to balance the accuracy and the risk of missed capture, especially in complex environments where the risk of missed capture is exacerbated. Summary of the invention

[0012] The present invention provides a train positioning method based on ultra-wideband enhanced Beidou and virtual transponder to achieve accurate capture of the target virtual transponder by the train, providing higher guarantee for continuous positioning and safety monitoring of on-board equipment.

[0013] In order to achieve the above object, the present invention adopts the following technical scheme.

[0014] A train positioning method based on ultra-wideband enhanced Beidou and virtual balise, comprising:

[0015] Establish a virtual balise basic database and an ultra-wideband device basic database based on the collected data of the target track line;

[0016] Establish a basic database of positioning observation quality based on the Beidou positioning observation quality of the target track line, and compile an ultra-wideband / Beidou joint positioning strategy set based on prior terrain environment information, Beidou positioning observation quality exploration information, and historical train operation data along the line;

[0017] During the operation of the train, a positioning strategy is dynamically selected in the ultra-wideband / Beidou joint positioning strategy set according to the real-time observation conditions of the Beidou signal, the three-dimensional spatial position of the current train is calculated, and multiple ultra-wideband base station observation data sets are extracted according to the current running mileage of the train and the ultra-wideband device spatial data in the ultra-wideband device basic database to obtain the ultra-wideband positioning solution data source of the train;

[0018] Constructing a train state characteristic parameter sequence, establishing a train mileage prediction network model, inputting the train state characteristic parameter sequence during the real-time operation of the train into the train mileage prediction network model, and obtaining the train mileage information at the next moment;

[0019] Extracting information related to the current train and the mileage information of the train at the next moment from the virtual balise basic database, determining the train mileage and pre-capture triggering moment of the next positioning solution cycle, calculating the predicted position of the train closest to the nearest target virtual balise from the pre-capture triggering moment, estimating the capture time difference of the nearest target virtual balise, and determining the capture moment of the nearest target virtual balise to be captured;

[0020] The position correction information is sent at the capture moment of the nearest target virtual balise to be captured, and the train corrects the current position according to the position correction information, while updating the real-time state sequence of the virtual balise.

[0021] Preferably, the step of establishing a virtual transponder basic database and an ultra-wideband device basic database based on the collected target track line data includes:

[0022] Set the target operation route from the starting station A to the terminal station B, and its mileage range is from the starting station center mileage S A Mileage to terminal station center S B , build a basic database of virtual transponders and ultra-wideband equipment covering the entire mileage range of the line;

[0023] The virtual transponder basic database includes track line spatial data, virtual transponder identity sequence and virtual transponder spatial data. The track line spatial data describes the spatial data of the track line between the starting station A and the terminal station B. Combined with the actual positioning measurement, data processing and format conversion of several key points on each track line in the interval and station, a complete track line spatial database is formed according to a specific data item format. The database stores the spatial information and attribute information of all key points of the line. index,λ The spatial data items stored at the key points of the route include: the key point mileage S (P index,λ ), the three-dimensional coordinate position of the key points of the line (L index,λ ,B index,λ ,H index,λ ), the stored attribute data items include: running direction D (P index,λ ), terrain environment attributes U(P index,λ ), the subscript index indicates the key point number of the route, the subscript λ indicates the route number, and the key point mileage S(P index ) is directly related to the uplink and downlink directions of the line. The mileage in the uplink direction decreases, while the mileage in the downlink direction increases. For any two adjacent key points of the line, the numbers are P α , P α+1 , the following conditions are met:

[0024] dis{P α (L α ,B α ,H α ),P α+1 (L α+1 ,B α+1 ,H α+1 )}-|S(P α )-S(P α+1 )|≤h s

[0025] Where dis{*} represents the straight-line distance solution function between two points based on three-dimensional space coordinates, h S is the tolerable orbital space data error limit;

[0026] The virtual transponder identity sequence describes the interval from the origin station A to the terminal station B and all N AB The identity data of the virtual transponder includes: target line index λ, direction flag D N and virtual transponder number b i ;

[0027] The virtual transponder space data items include: virtual transponder number b i , Virtual transponder mileage and the three-dimensional coordinate position of the virtual transponder

[0028] The ultra-wideband basic database contains ultra-wideband equipment spatial data, specifically including ultra-wideband base station number A j 、Ultra-wideband base station mileage and the three-dimensional coordinates of the ultra-wideband base station

[0029] Preferably, the method of establishing a basic database of positioning observation quality according to the Beidou positioning observation quality of the target track line, and compiling an ultra-wideband / Beidou joint positioning strategy set according to prior terrain environment information, Beidou positioning observation quality exploration information, and historical train operation data along the line includes:

[0030] The historical driving data along the target track line is used to explore the quality of Beidou positioning observations, and a basic positioning observation quality database is established to store Beidou positioning quality observation data, including: Beidou satellite signal-to-noise ratio mean SNR avg (P index,λ ), the number of visible BeiDou satellites n (P index,λ ), position precision dilution PDOP (P index,λ ), average pseudorange residual e P,avg (P index,λ ), Beidou satellite signal-to-noise ratio standard deviation σ SNR (P index,λ ) and the mean SNR of the Beidou satellite signal-to-noise ratio drop (P index,λ );

[0031] Based on the Beidou positioning observation quality exploration information and the historical train operation data of multiple lines, the possible Beidou observation quality conditions of the target track line are analyzed, including normal Beidou signal quality, degraded Beidou signal quality and Beidou signal failure.

[0032] The positioning strategy content Method contained in the ultra-wideband / Beidou joint positioning strategy set is determined according to the common Beidou observation quality of the line. The positioning strategy content Method includes three types: ultra-wideband independent positioning (UWB), Beidou independent positioning (GNSS), and ultra-wideband / Beidou joint positioning (UWB / GNSS).

[0033] Preferably, the method of dynamically selecting a positioning strategy in the ultra-wideband / Beidou joint positioning strategy set according to the real-time observation conditions of Beidou signals during the operation of the train to calculate the three-dimensional spatial position of the current train operation includes:

[0034] During the real-time operation of the train, at time t, the train control onboard equipment receives the signal from the Beidou positioning receiver and processes it to obtain the current navigation Beidou ephemeris and the pseudo-ranges of n visible Beidou satellites {ρ1(t), ρ2(t), …, ρ n (t)} real-time observation data, using the extracted spatial data of m UWB devices to obtain the relative distance information between the device and the current train {d1(t), d2(t), …, d m (t)};

[0035] In each positioning solution cycle, the system determines whether the Beidou signal meets the availability conditions:

[0036] SNR avg ≥SNR min And n≥n min

[0037] Determine the signal quality status of the Beidou signal that meets the availability conditions, and dynamically select a positioning strategy in the UWB / Beidou joint positioning strategy set based on the signal quality status of the Beidou signal

[0038] C) Signal quality is normal

[0039] PDOP≤PDOP max And e p,avg ≤e p,max and

[0040] If the conditions are met, the Beidou independent positioning strategy is selected;

[0041] D) Signal quality degradation due to occlusion

[0042] PDOP>PDOP max or e p,avg >e p,max

[0043] If the conditions are met, the UWB / Beidou joint positioning strategy is selected;

[0044] C) Signal quality degradation due to interference

[0045] or SNR drop >SNR drop,max

[0046] If the conditions are met, the UWB / Beidou joint positioning strategy is selected in real time;

[0047] If the system determines that the Beidou signal does not meet the availability conditions, the ultra-wideband independent positioning strategy is selected;

[0048] Construct the measurement vector z according to the selected positioning strategy and real-time observation information t The train's three-dimensional position {X(t), Y(t), Z(t)} and the clock error Δt constitute the state vector to be estimated, and the nonlinear state filter is used to calculate the estimated value of the state vector Get the 3D position of the train

[0049] Preferably, the extracting of multiple ultra-wideband base station observation data sets according to the current running mileage of the train and the ultra-wideband device spatial data in the ultra-wideband device basic database to obtain the ultra-wideband positioning solution data source of the train includes:

[0050] When the Beidou signal quality is normal, the Beidou independent positioning strategy is used to construct the measurement vector as follows:

[0051] z t =[ρ1(t),ρ2(t),…,ρ n (t)] T

[0052] When the BeiDou signal is still available but the quality is degraded, the UWB / BeiDou joint positioning strategy is used to construct the measurement vector:

[0053] z t =[ρ1(t),ρ2(t),…,ρ n (t),d1(t),d2(t),…,d m (t)] T

[0054] When the Beidou signal fails, the ultra-wideband independent positioning strategy is used to construct the measurement vector:

[0055] z t =[d1(t),d2(t),…,d m (t)] T

[0056] The state vector to be estimated is composed of the three-dimensional position of the train {X(t), Y(t), Z(t)} and the clock error Δt:

[0057] xt =[X(t),Y(t),Z(t),Δt] T

[0058] Combined with the three-dimensional coordinate positions of each visible Beidou satellite {X i (t),Y i (t),Z i (t)} Write the pseudo-range observation model based on the simplified three-dimensional coordinate position of the connected ultra-wideband device Write down the range-finding model and use the nonlinear Kalman filter to calculate the state vector estimate State vector estimate The specific process includes the following steps:

[0059]

[0060]

[0061] P t =(IK t H t ) t,t-1

[0062] in, P t,t-1 are the state vector prediction value and the one-step prediction variance matrix, respectively. t,t-1 is the state transfer matrix, K t is the equivalent filter gain matrix, Q t , R t are the variance matrices of system error and measurement error, respectively, t is the linearized equivalent measurement matrix obtained by transforming the observation model, P t is the state estimation variance matrix;

[0063] Set up dynamic iterative optimization judgment logic, and use the current train operation information and observation information at time t to iteratively optimize and obtain the optimal solution for train position estimation Estimate the optimal train position Convert to coordinate position Get the track key point P closest to the current position of the train index,λ , determine the train's running direction and line number;

[0064] The state vector estimate obtained by Kalman filtering is used as the initial state of the dynamic iterative optimization judgment logic. Set the optimization objective function X * To minimize the residual sum of squares, according to the three different strategies in the selected ultra-wideband / Beidou joint positioning, three optimization objective functions are specifically corresponding to each other:

[0065]

[0066] Where: e UWB,j =d j -||xp UWB,j || is the ranging residual of the jth ultra-wideband base station, d j is the actual measured ultra-wideband ranging distance, x is the train position state to be optimized, and p UWB,j are the location coordinates of the known ultra-wideband base station; is the pseudorange residual of the i-th Beidou satellite, is the Doppler velocity residual; and is the corresponding covariance matrix; x GNSS and x UWB is the train position state to be optimized calculated by Beidou and UWB, e Fus is the Euclidean distance difference between Beidou and UWB positioning results, e Fus =||x GNSS -x UWB ||, ∑Fus is the covariance of the fusion residual;

[0067] Use the nonlinear least squares method to perform state iterative optimization to determine whether the optimization accuracy is met:

[0068] ||e(x (k) )-e(x (k-1) )||<ε r

[0069] In the formula, ε r is the maximum acceptable variation of the residual error. If the above formula is satisfied, the optimal solution of the optimized train position estimation is output. If the above formula is not satisfied and the maximum number of iterations has been reached, the iteration is stopped and the current optimal estimation result is output;

[0070] Depend on Iteratively solve the corresponding geographic coordinates of the train Based on the current coordinate position of the train Determine the track key point P closest to the current position of the train index,λ , thereby determining the train's running direction D(t) and the line number λ;

[0071] Based on the train running status, the track line spatial data in the virtual balise basic database is extracted, and the optimal train position is estimated based on the calculated Use the nearest neighbor principle to find the segment between key points of the track closest to the optimal position estimate in the track spatial data Calculate the current running mileage of the train S(t):

[0072] S(t)=S(PF )±γ||P F -P E ||

[0073] Where S(P F ) is the key point P of the line F Mileage, γ is the projection coefficient, ||P F -P E || is a line segment length;

[0074] At the time t when the nearest virtual transponder to be captured is captured VB , use the correction information provided by the nearest virtual balise to be captured to update the current position of the train, and replace the estimated coordinates of the current optimal position of the train with the three-dimensional coordinate position of the nearest virtual balise to be captured Update mileage at the same time Implement correction information integration in the position calculation process;

[0075] According to the current mileage S(t) of the train and the spatial data of the ultra-wideband equipment in the basic database of ultra-wideband equipment, multiple ultra-wideband base station observation data sets are extracted according to the distance principle to obtain the ultra-wideband positioning solution data source of the train.

[0076] Preferably, the step of constructing a train state characteristic parameter sequence, establishing a train mileage prediction network model, inputting the train state characteristic parameter sequence during the real-time operation of the train into the train mileage prediction network model, and obtaining the mileage information of the train at the next moment includes:

[0077] Using the length W len The sliding window extracts the mathematical and statistical characteristics of train operation data from the historical driving data along the target track line, and constructs the train state characteristic parameter sequence V t , establish a sample set containing time series information, where each sample contains sequence data of train state characteristic parameters, and the mathematical statistical characteristics of the train operation data include the current train position x t , train speed v t , train acceleration a t , Train mileage ΔS t , the train's cumulative mileage S t , mean train speed Mean train acceleration Train speed maximum sequence v t,max , Train acceleration maximum sequence a t,max And the standard deviation v of the velocity and acceleration extreme value series t,std 、a t,std , extreme value v t,range 、at,range And the ratio of the maximum value to the minimum value v t,rate 、a t,rate ;

[0078] Then the train state characteristic parameter sequence is:

[0079]

[0080] The train state characteristic parameter sequence is normalized by the min-max normalization method to obtain a continuous W len The characteristic parameter sequence of the positioning solution cycle is used as the input of the train mileage prediction network model, and the predicted train mileage in the next positioning solution cycle is used as the output. The characteristic sequence is input into the train mileage prediction network model f(·). The model extracts and calculates the features according to the input sequence, and calculates the change trend of the train driving state layer by layer. Through the training of the model, the relationship between the characteristic parameter sequence and the mileage is learned, so as to generate the mileage information ΔS of the train in the next positioning solution cycle ΔT. t ' +1 The specific process includes the following steps:

[0081] (1) Let the input sequence V = [V1, V2, V3, ..., V t ]

[0082] (2) The input sequence V undergoes a nonlinear transformation to generate a hidden state sequence H (1)

[0083]

[0084] (3) Using the first hidden state H (1) As input, after the second layer transformation, a new hidden state sequence H is obtained. (2)

[0085]

[0086] (4) According to Q = XW Q , K = XW K ,P=XW P Generate Query, Key and Value matrices, where W Q , W K , W P is a trainable weight matrix; then calculate the dynamic weight and weight A,

[0087]

[0088] The softmax function is used to standardize the weight value to the [0,1] interval;

[0089] (5) Perform weighted summation on the Value matrix P according to the dynamic weight and weight A to obtain the weighted aggregation output H agg :

[0090] H agg =AP

[0091] The eigenvector H agg Input into the fully connected layer to generate the predicted mileage ΔS′ of the train t+1

[0092] ΔS′ t+1 =W o H agg +b o

[0093] Where W o and b o are the weights and biases of the fully connected layer respectively;

[0094] (6) Use mean square error (MSE) as the loss function to calculate the error between the predicted mileage and the actual mileage:

[0095]

[0096] Where N is the number of samples, ΔS t+1 is the actual running mileage of the train;

[0097] Select mean square error As the target loss function, where N is the number of samples, ΔS t+1 Based on the actual running mileage of the train, the model is trained iteratively multiple times to improve its ability to predict the running status of the train until the model converges;

[0098] For the train mileage prediction network model obtained through training after convergence, multiple train historical operation data are selected as the test set, and the model parameters are adjusted according to the model prediction accuracy to achieve model optimization. The specific structure of the train mileage prediction network model is as follows:

[0099] V=[V1,V2,V3,...,V t ]

[0100]

[0101] Q=XW Q , K = XW K ,P=XW P

[0102]

[0103] H agg =AP

[0104] ΔS′ t+1 =W o H agg +b o

[0105] Among them, f (1) is the activation function of the first layer, θ (1) is the weight and bias set of the first layer network, f (2) is the activation function of the second layer, θ (2) is the weight and bias set of the second layer network, W Q , W K , W P is a trainable weight matrix, A is a dynamic weighted sum weight, W o and b o are the weight and bias of the fully connected layer respectively.

[0106] Preferably, the extracting of information related to the current train and the mileage information of the train at the next moment from the virtual transponder basic database, determining the train mileage and pre-capture triggering moment of the next positioning solution cycle, calculating the predicted position of the train closest to the nearest target virtual transponder from the pre-capture triggering moment, estimating the capture time difference of the nearest target virtual transponder, and determining the capture moment of the nearest target virtual transponder to be captured, includes:

[0107] According to the current train running direction D(t), extract the virtual balise identity sequence in the virtual balise basic database to construct the virtual balise real-time state sequence, and determine the virtual balise number b closest to the target to be captured in this direction i The virtual transponder real-time status sequence describes the interval from the starting station A to the terminal station B and all N contained in the station AB The state information data of the virtual transponder is obtained by capturing the state Acq(i,D N ), where i represents the virtual transponder number, i = 1, 2, ..., N AB , D N It is used to mark the actual running direction of the train in the current balise capture process, Acq(i,D N ) takes a value of 0, indicating that the virtual transponder is not captured; Acq(i,D N ) takes a value of 1, indicating that the virtual transponder has been captured;

[0108] According to the number b of the nearest target virtual transponder to be captured i Extract the corresponding virtual transponder spatial data and determine the target virtual transponder mileage information When the predicted running mileage of the next positioning solution cycle of the train calculated at time t meets the following conditions, the current time t is determined as the pre-capture trigger time of the target virtual balise to be captured

[0109] ΔS′ t+1 ≥ΔS NVB

[0110] Among them, ΔS NVB is the distance between the train and the target virtual balise at the current moment;

[0111] In the unit positioning solution cycle ΔT, multi-step forward prediction is performed at fixed time intervals δt, and the train mileage S′(t+kδt), k=1,2,...,N-1, Compute the predicted position of the train at each time step.

[0112] The train mileage S′(t+kδt) is predicted in multiple steps forward at a time interval δt. The input feature sequence is constructed according to the current train status and historical status. The feature sequence is used as the initial input of the model for the first forward prediction. The model outputs the predicted mileage S′(t+δt) of the first time step, which represents the mileage of the train within the time interval δt. Based on the prediction of the first time step, the prediction result is recursively used as the input of the next time step to continue the multi-step forward prediction. In each recursion, the model outputs the mileage of the next time step, and the predicted mileage sequence of multiple future time steps is obtained {S′(t+δt), S′(t+2δt), ..., S′(t+(N-1)δt)}.

[0113] Calculate the train running mileage S′(t+kδt) and the target virtual balise mileage at each time step The distance ΔS k (ΔS k =|S′(t+kδt)-S(t)|), among all the predicted moments, find the predicted train position closest to the target virtual balise position, i.e., ΔS k The minimum time t+kδt, ​​the target virtual transponder capture time difference kδt is obtained;

[0114] The judgment time t+kδt is numbered b i The capture time T of the nearest virtual transponder to be captured cap , that is, T cap =t+kδt.

[0115] Preferably, the sending of position correction information at the capture time of the nearest target virtual balise to be captured, the train correcting the current position according to the position correction information, and updating the real-time state sequence of the virtual balise at the same time, comprises:

[0116] Extract the three-dimensional coordinate position of the captured virtual transponder closest to the target to be captured and mileage Get the train's current position correction information;

[0117] At the capture time of the nearest target virtual transponder to be captured, the real-time state sequence of the virtual transponder is updated, and the capture state Acq(b i ,D N ) is captured;

[0118] Along the actual running direction of the train, select the next virtual balise closest to the front as the closest target virtual balise to be captured in the subsequent running process of the system, and update the number of the closest target virtual balise to be captured to b i+1 .

[0119] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the method of the present invention combines ultra-wideband technology with Beidou positioning, avoiding the problem of difficult balance between capture accuracy and risk of missed capture in conventional capture solutions from the source, ensuring continuous and stable high-precision positioning regardless of whether Beidou signals are available in complex environments, eliminating the availability limitations of virtual transponder capture, and effectively improving the capture accuracy and stability of virtual transponders in complex environments.

[0120] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0122] Figure 1 It is a schematic flow chart of a virtual transponder acquisition method based on ultra-wideband enhanced Beidou described in an embodiment of the present invention;

[0123] Figure 2 It is a schematic diagram of the principle of extracting observation data sets of multiple ultra-wideband base stations described in an embodiment of the present invention;

[0124] Figure 3It is a schematic diagram of the regional enhancement mechanism provided by the ultra-wideband unit under Beidou restricted conditions described in an embodiment of the present invention;

[0125] Figure 4 It is a schematic diagram of the principle of determining the pre-capture triggering time of the nearest target virtual transponder to be captured described in an embodiment of the present invention;

[0126] Figure 5 It is a schematic diagram of the principle of predictive capture and identification of the nearest virtual transponder to be captured described in an embodiment of the present invention;

[0127] Figure 6 Schematic diagram of the capture process and capture error of the virtual transponder in an embodiment of the present invention;

[0128] Figure 7 It is a schematic diagram comparing the capture solution of the embodiment of the present invention and the traditional capture solution. DETAILED DESCRIPTION

[0129] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0130] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0131] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0132] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0133] In order to solve the problem of seamless capture of virtual transponders in the complex environment of wide-area railway network, the present invention provides a virtual transponder capture method based on ultra-wideband enhanced Beidou. This method analyzes the possible quality of various Beidou observations of the target track line, sets a positioning solution logic adjustment and reconstruction mechanism, introduces ultra-wideband positioning technology to enhance Beidou positioning, and uses dynamic iterative optimization decision logic to suppress positioning errors to ensure the continuous provision of real-time train positioning information; at the same time, the train mileage prediction network model is used to predict the train running mileage of the next positioning cycle as the basis for capture judgment, avoiding the problem of difficult balance between capture accuracy and missed capture risk in conventional capture solutions from the source, eliminating the availability limitation of virtual transponder capture, and effectively supporting the design, development and equipment manufacturing of new train control systems.

[0134] This embodiment provides a processing flow of a virtual transponder acquisition method based on ultra-wideband enhanced Beidou. Figure 1 As shown, the processing steps include the following:

[0135] Step S1, collecting data of the target track line, and establishing a virtual transponder basic database and an ultra-wideband device basic database according to the collected data;

[0136] Step S2, exploring the Beidou positioning observation quality of the target track line to establish a basic database of positioning observation quality, and compiling an ultra-wideband / Beidou joint positioning strategy set based on prior terrain environment information, Beidou positioning observation quality exploration information, and historical train operation data along the line;

[0137] Step S3, during the operation of the train, dynamically select a positioning strategy from the ultra-wideband / Beidou joint positioning strategy set according to the real-time observation conditions of the Beidou signal, adjust and reconstruct the positioning solution logic, calculate the three-dimensional spatial position of the current train, use dynamic iterative optimization decision logic to suppress positioning errors, calculate the current mileage of the train and the ultra-wideband device spatial data in the ultra-wideband device basic database, extract multiple ultra-wideband base station observation data sets, and obtain the ultra-wideband positioning solution data source.

[0138] Step S4, constructing a train state characteristic parameter sequence, establishing a train mileage prediction network model, inputting the train state characteristic parameter sequence during the real-time operation of the train into the train mileage prediction network model, and obtaining the train mileage information at the next moment;

[0139] Step S5, extracting information related to the current train from the virtual transponder basic database, determining the train mileage and pre-capture trigger time of the next positioning solution cycle, calculating the predicted position of the train closest to the nearest target virtual transponder from the pre-capture trigger time, estimating the capture time difference of the nearest target virtual transponder, and determining the capture time of the nearest target virtual transponder to be captured;

[0140] Step S6, sending position correction information at the time of capturing the nearest target virtual balise to be captured, the train corrects the current position according to the position correction information, and updates the real-time status sequence of the virtual balise at the same time.

[0141] Wherein, the step S1 collects the data of the target track line to establish a virtual transponder basic database and an ultra-wideband device basic database, which specifically includes:

[0142] Combined with the actual situation of the target operation line, a mileage range covering the entire line (from the starting station A to the terminal station B, the mileage range is from the starting station center mileage S A Mileage to terminal station center S B )'s virtual transponder basic database and ultra-wideband device basic database.

[0143] 1) Virtual balise basic database, including track line spatial data, virtual balise identity sequence, and virtual balise spatial data:

[0144] The track line spatial data mainly describes the spatial data of the track line between the starting station A and the terminal station B. It is combined with the actual positioning measurement, data processing and format conversion of several key points on each track line in the interval and station, and is organized according to a specific data item format to form a complete track line spatial database. The database mainly stores the spatial information and attribute information of all key points on the line. index,λ The key points of the route, the main stored spatial data items include: route key point mileage S (P index,λ ), the three-dimensional coordinate position of the key points of the line (L index,λ ,B index,λ ,H index,λ ), the main attribute data items stored include: running direction D (P index,λ ), terrain environment attributes U(P index,λ ). Among them, the subscript index represents the key point number of the line, the subscript λ represents the line number, and the key point mileage S(P index) is directly related to the up and down directions of the line. The mileage in the up direction decreases, while the mileage in the down direction increases. The spatial data accuracy of the key points of the track line directly determines the capture performance of the virtual balise. The accuracy level of the track line spatial database must meet the line segment approximation error requirements, that is, for any two adjacent key points of the line, the numbers are P and α , P α+1 , the following conditions are met:

[0145] dis{P α (L α ,B α ,H α ),P α+1 (L α+1 ,B α+1 ,H α+1 )}-|S(P α )-S(P α+1 )|≤h S

[0146] Where dis{*} represents the straight-line distance solution function between two points based on three-dimensional space coordinates, h S is the tolerable orbital space data error limit.

[0147] The virtual transponder identity sequence mainly describes the interval from the starting station A to the terminal station B and all N AB The identity information data of a virtual balise. For double-track lines and areas with multiple tracks, it is necessary to use line index number, default running direction and other information to mark the virtual balise identity. The virtual balise identity sequence mainly includes the identity data of the virtual balise, where: the virtual balise identity data includes: target line index number λ, direction flag bit D N , virtual transponder number b i , based on this data, the identity of any virtual transponder can be uniquely determined;

[0148] The virtual transponder space data is collected and processed in advance to ensure the uniqueness of the virtual transponder position. The virtual transponder space data items mainly include: virtual transponder number b i , Virtual transponder mileage Virtual transponder 3D coordinate position

[0149] 2) Ultra-wideband basic database, including ultra-wideband equipment spatial data, including ultra-wideband base station number A j 、Ultra-wideband base station mileage Ultra-wideband base station 3D coordinates

[0150] The UWB equipment spatial data is generally collected and processed synchronously during the measurement and processing of the track line spatial data and the virtual transponder spatial data to ensure the uniqueness of the UWB equipment location. Similar to the virtual transponder spatial data, the UWB equipment spatial data includes the UWB base station number A j 、Ultra-wideband base station mileage Ultra-wideband base station 3D coordinates

[0151] The step S2 specifically includes the following steps:

[0152] Step S21, using the historical driving data along the target track line to explore the Beidou positioning observation quality, establish a positioning observation quality basic database, which is used to store Beidou positioning quality observation data, mainly Beidou positioning quality related information of key points of the line, usually carried out simultaneously during the line data survey and measurement process, specifically including: Beidou satellite signal-to-noise ratio mean SNR avg (P index,λ ), the number of visible BeiDou satellites n (P index,λ ), position precision dilution PDOP (P index,λ ), average pseudorange residual e P,avg (P index,λ ), Beidou satellite signal-to-noise ratio standard deviation σ SNR (P index,λ ), Beidou satellite signal-to-noise ratio reduction average SNR drop (P index,λ );

[0153] Step S22, based on the Beidou positioning observation quality exploration information and the historical train operation data of multiple lines, analyze the various possible Beidou observation quality conditions of the target track line, including three categories: normal Beidou signal quality, degraded Beidou signal quality, and Beidou signal failure. Among them, the reasons for the degradation and failure of Beidou signals can be divided into two factors: terrain shielding and interference. The situations that may occur in actual lines are only a subset of the above three Beidou signal quality situations. The specific process of Beidou observation quality category judgment is as follows:

[0154] (1) Beidou signal availability judgment: SNR avg ≥SNR min And n≥n min , where SNR avg is the mean signal-to-noise ratio of Beidou satellites, SNR i is the signal-to-noise ratio of the i-th visible Beidou satellite.

[0155] (2) If the signal meets the availability conditions, the signal quality is judged as follows: Beidou signal quality is normal, Beidou signal is degraded by terrain shielding, and Beidou signal is degraded by interference, corresponding to:

[0156] PDOP≤PDOP max And e p,avg ≤e p,max and

[0157] PDOP>PDOP max or e p,avg >e p,max

[0158] or SNR drop >SNR drop,max

[0159] Among them, PDOP is the position precision factor, which reflects the impact of the geometric distribution of Beidou satellites on positioning errors. is the coordinate of the i-th visible Beidou satellite.

[0160] (3) If the signal does not meet the availability conditions, the Beidou signal is directly judged to be invalid, and the cause of the failure is determined. It can be divided into terrain shielding failure and interference failure, corresponding to the following conditions:

[0161] And SNR drop ≤SNR drop,max

[0162] or SNR drop >SNR drop,max

[0163] in, is the standard deviation of Beidou satellite signal-to-noise ratio; SNR drop is the mean decrease in the Beidou satellite signal-to-noise ratio, calculated using a sliding window: Where: N t is the number of observations in the sliding window, SNR h (i) is the maximum value of the signal-to-noise ratio in the i-th sliding window, SNR l (i) is the minimum value of the signal-to-noise ratio in the i-th sliding window; SNR min is the minimum threshold of the Beidou satellite signal-to-noise ratio mean, n is the number of visible Beidou satellites, min PDOP is the minimum number of visible Beidou satellites. max is the maximum acceptable value of the position precision factor, e p,max is the maximum acceptable value of the pseudorange residual, σ SNR，max is the maximum acceptable value of Beidou satellite signal-to-noise ratio fluctuation, SNRdrop,max It is the maximum acceptable value of the Beidou satellite signal-to-noise ratio degradation.

[0164] According to the common Beidou observation quality of the line, the specific positioning strategy content contained in the ultra-wideband / Beidou joint positioning strategy set is determined accordingly, which mainly includes three types: ultra-wideband independent positioning (UWB), Beidou independent positioning (GNSS), and ultra-wideband / Beidou joint positioning (UWB / GNSS):

[0165]

[0166] In this step, the specific positioning strategy content contained in the ultra-wideband / Beidou joint positioning strategy set is determined, specifically including three strategies: ultra-wideband independent positioning (UWB), Beidou independent positioning (GNSS), and ultra-wideband / Beidou joint positioning (UWB / GNSS):

[0167] (1) Signal quality is normal (Method = GNSS)

[0168] Beidou signals have a high signal-to-noise ratio and stability, which can be seen when there are sufficient Beidou satellites; choose Beidou independent positioning strategy, that is, only use Beidou positioning technology to provide continuous, stable and accurate location information. In this case, ultra-wideband positioning is used as a backup and does not actively participate in positioning calculations;

[0169] (2) Signal quality degradation (Method = UWB / GNSS)

[0170] The Beidou signal has signal-to-noise ratio fluctuations, a decrease in the number of visible Beidou satellites, and an increase in PDOP, which leads to signal quality degradation. However, the signal is still usable. In this case, the UWB / Beidou joint positioning strategy is selected to improve positioning stability and accuracy.

[0171] (3) Beidou signal failure (Method = UWB)

[0172] When the BeiDou signal cannot be received normally, the signal-to-noise ratio is extremely low or fails, and the number of BeiDou satellites is far below the minimum requirement, it switches to the UWB independent positioning strategy and relies only on the UWB positioning technology to provide continuous train position information.

[0173] The step S3 specifically includes the following steps:

[0174] Step S31, during the operation of the train, the real-time observation information obtained by the Beidou positioning receiver and the ultra-wideband unit contained in the train control on-board equipment at the current time t is extracted in real time, and the current Beidou signal availability is determined according to the extracted real-time observation data and the Beidou observation quality category judgment process, and the positioning strategy is dynamically selected in the ultra-wideband / Beidou joint positioning strategy set; in this step, the real-time observation information obtained is used to collect Beidou signals in real time by using the Beidou positioning receiver contained in the train control on-board equipment. At the same time, in order to ensure the continuity of positioning, the relative distance information between the ultra-wideband unit and the train is synchronously collected in real time. In addition, the ultra-wideband unit is used to detect and diagnose the integrity of the Beidou positioning signal, and to assist in confirming whether the Beidou signal is interfered with, so that when the Beidou signal quality is degraded or the Beidou signal fails, the ultra-wideband unit and the Beidou positioning receiver are selected to realize the combined positioning solution or the ultra-wideband independent positioning solution is used to replace the Beidou independent positioning for the capture judgment of the virtual transponder.

[0175] The Beidou signal quality determination condition is used to dynamically select the specific positioning strategy of the ultra-wideband / Beidou joint positioning strategy set to adapt to the changes in Beidou signal quality, thereby ensuring the accuracy of positioning solution; the system uses the Beidou satellite signal-to-noise ratio mean SNR avg , the number of visible Beidou satellites n, the position precision factor PDOP, the average pseudorange residual e P,avg , Beidou satellite signal-to-noise ratio standard deviation σ SNR , Beidou satellite signal-to-noise ratio reduction average SNR drop To determine the quality of Beidou signals, the specific determination process is as follows:

[0176] (1) Real-time signal acquisition

[0177] At time t, the train control vehicle-mounted equipment receives the signal from the Beidou positioning receiver and processes it to obtain the current navigation Beidou ephemeris and the pseudo-ranges of n visible Beidou satellites {ρ1(t), ρ2(t), …, ρ n (t)} and other real-time observation information, and use the extracted spatial data of m ultra-wideband devices to obtain the relative distance information between the device and the current train {d1(t), d2(t), …, d m (t)};

[0178] (2) Real-time judgment of signal availability

[0179] During the real-time operation of the train, in each positioning solution cycle, the system determines whether the Beidou signal meets the availability conditions:

[0180] SNR avg ≥SNR min And n≥n min

[0181] If the conditions are met, the signal is determined to be available and the signal quality status is judged in real time. If the conditions are not met, the signal is directly determined to be invalid and the invalidation cause is judged (step (4)).

[0182] (3) Real-time judgment of signal quality status

[0183] For signals that meet availability conditions, the system further determines the signal quality status and determines the positioning strategy in real time:

[0184] 1: Signal quality is normal

[0185] PDOP≤PDOP max And e p,avg ≤e p,max and

[0186] If the conditions are met, the Beidou independent positioning strategy is selected in real time, and only Beidou signals are used for positioning;

[0187] 2: Signal quality is degraded due to terrain obstruction

[0188] PDOP>PDOP max or e p,avg >e p,max

[0189] If the conditions are met, the UWB / Beidou joint positioning strategy is selected in real time, using UWB signals and Beidou signals for joint positioning to improve positioning stability and accuracy;

[0190] 3: Signal quality is degraded due to interference

[0191] or SNR drop >SNR drop,max

[0192] If the conditions are met, the UWB / Beidou joint positioning strategy is selected in real time to cope with the degradation risk caused by signal interference;

[0193] 4: Real-time determination of failure causes

[0194] If the signal does not meet the availability conditions, the system directly determines that the Beidou signal is invalid and determines the specific cause of the failure in real time:

[0195] A) Signal failure due to terrain obstruction

[0196] And SNR drop ≤SNR drop,max

[0197] B) The signal fails due to interference

[0198] or SNR drop >SNR drop,max

[0199] If the conditions are met, the UWB independent positioning strategy is selected in real time, and UWB positioning is completely relied on to ensure the continuity of positioning information. After determining the Beidou signal quality, the system dynamically selects the positioning strategy in the "UWB / Beidou joint positioning strategy set" according to the following rules to adapt to the current signal quality status:

[0200] (1) Normal signal quality – Beidou independent positioning strategy

[0201] The system relies solely on Beidou signals for positioning. In this case, the Beidou signal quality is stable and no ultra-wideband assistance is required, saving computing and resources.

[0202] (2) Signal quality degradation – UWB / Beidou joint positioning strategy

[0203] In the case of signal degradation, the system selects the UWB / Beidou joint positioning strategy, introduces UWB data and Beidou signals for joint positioning and calculation, and ensures the accuracy and stability of positioning; UWB plays a reinforcing role in this case to compensate for the quality degradation of Beidou signals;

[0204] (3) Signal failure – ultra-wideband independent positioning strategy

[0205] When the Beidou signal fails, the system will completely switch to the ultra-wideband independent positioning strategy, relying on the ultra-wideband signal to provide positioning information, replacing the failed Beidou positioning, and ensuring the continuity of positioning information in the event of Beidou signal failure or severe interference;

[0206] Figure 3 It is a schematic diagram of the regional enhancement mechanism provided by the ultra-wideband unit under the Beidou restricted condition described in an embodiment of the present invention.

[0207] Step S32: Start the position calculation process to calculate the current three-dimensional spatial position of the train based on the selected positioning strategy and real-time observation information. Construct the measurement vector z t , which can be divided into three situations:

[0208] (1) When the Beidou signal quality is normal, the measurement vector is

[0209] z t =[ρ1(t),ρ2(t),…,ρ n (t)] T

[0210] (2) When the BeiDou signal is available but the BeiDou signal quality is degraded, the measurement vector is:

[0211] zt =[ρ1(t),ρ2(t),…,ρ n (t),d1(t),d2(t),…,d m (t)] T

[0212] (3) When the BeiDou signal fails, the measurement vector is:

[0213] z t =[d1(t),d2(t),…,d m (t)] T

[0214] The state vector to be estimated is composed of the three-dimensional position of the train {X(t), Y(t), Z(t)} and the clock error Δt: t =[X(t),Y(t),Z(t),Δt] T In the above three cases, the corresponding measurement vector z is used respectively. t , using a nonlinear Kalman filter to calculate the state vector estimate The specific process of filtering estimation includes the following steps:

[0215]

[0216] P t =(IK t H t ) t,t-1

[0217] in, P t,t-1 are the state vector prediction value and the one-step prediction variance matrix, respectively. t,t-1 is the state transfer matrix, K t is the equivalent filter gain matrix, Q t , R t are the variance matrices of system error and measurement error, respectively, t is the linearized equivalent measurement matrix obtained by transforming the observation model, P t is the state estimation variance matrix, so as to obtain the train three-dimensional coordinate position estimation result

[0218] In this step, the measurement vector is constructed according to the current Beidou signal quality observation and the positioning strategy selected for access:

[0219] When the Beidou signal quality is normal, the Beidou independent positioning strategy is used to construct the measurement vector:

[0220] z t =[ρ1(t),ρ2(t),…,ρ n (t)]T

[0221] When the BeiDou signal is still available but the quality is degraded, the UWB / BeiDou joint positioning strategy is used to construct the measurement vector:

[0222] z t =[ρ1(t),ρ2(t),…,ρ n (t),d1(t),d2(t),…,d m (t)] T

[0223] When the Beidou signal fails, the ultra-wideband unit is used to replace the Beidou, and the ultra-wideband independent positioning strategy is used to construct the measurement vector:

[0224] z t =[d1(t),d2(t),…,d m (t)] T

[0225] At the same time, the state vector to be estimated is formed by the three-dimensional position of the train {X(t), Y(t), Z(t)} and the clock error Δt:

[0226] x t =[X(t),Y(t),Z(t),Δt] T

[0227] Combined with the three-dimensional coordinate positions of each visible Beidou satellite {X i (t),Y i (t),Z i (t)} Write the pseudo-range observation model based on the simplified three-dimensional coordinate position of the connected ultra-wideband device Write down the range-finding model and use the nonlinear Kalman filter to calculate the state vector estimate The specific process of filtering estimation includes the following steps:

[0228]

[0229] P t =(IK t H t ) t,t-1

[0230] in, P t,t-1 are the state vector prediction value and the one-step prediction variance matrix, respectively. t,t-1 is the state transfer matrix, K t is the equivalent filter gain matrix, Q t , R t are the variance matrices of system error and measurement error, respectively, tis the linearized equivalent measurement matrix obtained by transforming the observation model, P t is the state estimation variance matrix, so as to obtain the train three-dimensional coordinate position estimation result

[0231] Step S33: Using the train operation information and observation information at the current time t, enter the position calculation process optimization stage, set the dynamic iterative optimization judgment logic, and iterate and optimize to obtain the optimal solution for train position estimation. Estimate the optimal train position Convert to coordinate position Get the track key point P closest to the current position of the train index,λ , determine its running direction and line number.

[0232] The state vector estimate obtained by Kalman filtering is used as the initial state of the dynamic iterative optimization judgment logic. Set the optimization objective function X * To minimize the residual sum of squares, according to the three different strategies in the selected ultra-wideband / Beidou joint positioning, three optimization objective functions are specifically corresponding to each other:

[0233]

[0234] Where: e UWB,j =d j -||xp UWB,j || is the ranging residual of the jth ultra-wideband base station, d j is the actual measured ultra-wideband ranging distance, x is the train position state to be optimized, and p UWB,j are the location coordinates of the known ultra-wideband base station; is the pseudorange residual of the i-th Beidou satellite, is the Doppler velocity residual; and is the corresponding covariance matrix; x GNSS and x UWB is the train position state to be optimized calculated by Beidou and UWB, e Fus is the Euclidean distance difference between Beidou and UWB positioning results (e Fus =||x GNSS -x UWB ||), ∑Fus is the covariance of the fusion residual;

[0235] On this basis, the nonlinear least squares method is used to perform state iterative optimization to determine whether the optimization accuracy is met:

[0236] ||e(x (k) )-e(x (k-1) )||<ε r

[0237] In the formula, ε r is the maximum acceptable variation of the residual error. If the above formula is satisfied, the optimal solution of the optimized train position estimation is output. If the above formula is not satisfied and the maximum number of iterations has been reached, the iteration is stopped and the current optimal estimation result is output;

[0238] In this step, positioning error is suppressed through dynamic iterative optimization logic. At the current time t, the initial state estimate obtained by inputting the Kalman filter is and various observables z k , including Beidou pseudorange ρ, Beidou clock error Δt, ultra-wideband ranging information d, etc., are used to build the relationship between state variables and observed quantities; set up dynamic iterative optimization logic, and use the state estimation value obtained by Kalman filtering as the initial state of the dynamic iterative optimization logic Set the optimization objective function X * To minimize the residual sum of squares, according to the three different strategies in the selected ultra-wideband / Beidou joint positioning, three optimization objective functions are specifically corresponding to each other, and the specific optimization objective function is:

[0239]

[0240] Use nonlinear least squares method to perform state iterative optimization to determine whether sufficient optimization accuracy has been achieved:

[0241] ||e(x (k) )-e(x (k-1) )||<ε r

[0242] In the formula, ε r is the maximum acceptable variation of the residual. If the above formula is satisfied, the optimized state estimate is output. If the above formula is still not satisfied when the maximum number of iterations is reached, the iteration is stopped and the current optimal estimation result is output.

[0243] Depend on Iteratively solve its corresponding geographic coordinates The reference ellipsoid is selected as WGS-84 ellipsoid, and the major axis a is defined as 6378137.0m, the flattening f is defined as 1 / 298.257223563, and the coordinates of the three-dimensional space point are (X * ,Y * ,Z * ), the specific conversion process is as follows:

[0244]

[0245]

[0246] (Meet the accuracy requirements | B i+1 -B i | < ε, where ε = 10 -10 )

[0247] B≈B i+1 ,

[0248] Determine the running direction D(t), the line number λ, extract the track line spatial data in the virtual balise basic database, based on the current coordinate position of the train Determine the track key point P closest to the current position of the train index,λ , thereby determining its running direction and line number.

[0249] Step S34, based on the train running status, extract the track line space data in the virtual balise basic database, and estimate the optimal train position according to the calculated Use the nearest neighbor principle to find the segment between key points of the track closest to the optimal position estimate in the track spatial data Calculate the current running mileage of the train S(t):

[0250] S(t)=S(P F )±γ||P F -P E ||

[0251] Where S(P F ) is the key point P of the line F Mileage (upward mileage decreases, downward mileage increases), γ is the projection coefficient, ||P F -P E || is a line segment length;

[0252] At the time t when the nearest virtual transponder to be captured is captured VB , use the correction information provided by it to update the current position of the train, and replace the current train's optimal position estimate coordinates with the three-dimensional coordinate position of the virtual balise of the nearest target to be captured Update mileage at the same time Implement correction information integration in the position calculation process;

[0253] According to the current mileage S(t) of the train and the spatial data of the ultra-wideband equipment in the basic database of ultra-wideband equipment, multiple ultra-wideband base station observation data sets are extracted according to the distance principle to obtain the data source of the ultra-wideband positioning solution.

[0254] In this step, at the current time t, the track line spatial data in the virtual balise database is extracted, and the optimal train position is estimated based on the calculated In the track line spatial data, the nearest neighbor principle of the key point segment is used to search and determine the line key point segment associated with the current position. The segment consists of two adjacent line key points P. F , P E The straight line segment formed is uniquely determined. On this basis, by calculating the current position Clip between route keypoints The projection position within the line is calculated using the line key point P F Mileage S(P F ) and the offset in the segment of the projection position, calculate the current running mileage of the train S(t) = S(P F )±γ||P F -P E ||, where γ is the projection coefficient, ||P F -P E || is a line segment length;

[0255] The multi-UWB base station observation data set extraction screens all devices that establish signal transmission relationships with the train within the range in front of the train according to the distance priority principle, discards devices that are far away, and only retains devices that meet the distance requirements to avoid signal quality degradation. Figure 2 The diagram is a schematic diagram of the principle of extracting observation data sets of multiple ultra-wideband base stations in this embodiment.

[0256] The step S4 specifically includes the following steps:

[0257] Step S41, using a sliding window (length W len ) Extract the mathematical and statistical characteristics of train operation data from the historical driving data along the target track line and construct the train state characteristic parameter sequence V t , establish a sample set containing time series information, where each sample contains sequence data of train state characteristic parameters, and the mathematical statistical characteristics of the train operation data include the current train position x t , train speed v t , train acceleration a t , Train mileage ΔS t , the train's cumulative mileage S t , mean train speed Mean train acceleration Train speed maximum sequence v t,max , Train acceleration maximum sequence a t,max And the standard deviation v of the velocity and acceleration extreme value series t,std 、a t,std , extreme value v t,range 、a t,range And the ratio of the maximum value to the minimum value vt,rate 、a t,rate ;

[0258] In this step, set the sliding window W len , based on the historical driving data along the target track line, the mathematical and statistical characteristics of train operation data are calculated, and the train state characteristic parameter sequence V is constructed t , establish a sample set containing time series information, the mathematical and statistical characteristics of the train operation data include the current train position x t , train speed v t , train acceleration a t , Train mileage ΔS t And the train's cumulative mileage S t , take the average speed at each moment in the sliding window and the mean acceleration a t Composition sequence;

[0259] Take the maximum value of the speed and acceleration sequence at each moment in the sliding window to form the maximum speed sequence v of the train operation data t,max and the acceleration maximum sequence a t,max , calculate the standard deviation v of the two sequences respectively t,std 、a t,std , extreme value v t,range 、a t,range And the ratio of the maximum value to the minimum value v t,rate 、a t,rate ;

[0260] Then the train state characteristic parameter sequence is:

[0261]

[0262] Step S42, normalize the train state characteristic parameter sequence by using the min-max normalization method to obtain a continuous W len The characteristic parameter sequence of the positioning solution cycle is used as the input of the train mileage prediction network model, and the predicted train mileage in the next positioning solution cycle is used as the output. The train mileage prediction network model f(·) is constructed to predict the train mileage information at the next moment. The mean square error is selected. As the target loss function, where N is the number of samples, ΔS t+1 Based on the actual running mileage of the train, the model is trained iteratively multiple times to improve its ability to predict the train running status until the model converges.

[0263] For the train mileage prediction network model obtained through training after convergence, multiple train historical operation data are selected as the test set, and the model parameters are adjusted according to the model prediction accuracy to achieve model optimization. The specific structure of the train mileage prediction network model is as follows:

[0264] V=[V1,V2,V3,...,V t ]

[0265]

[0266] Q=XW Q , K = XW K ,P=XW P

[0267]

[0268] H agg =AP

[0269] ΔS′ t+1 =W o H agg +b o

[0270] Among them, f (1) is the activation function of the first layer, θ (1) is the weight and bias set of the first layer network, f (2) is the activation function of the second layer, θ (2) is the weight and bias set of the second layer network, W Q , W K , W P is a trainable weight matrix, A is a dynamic weighted sum weight, W o and b o are the weights and biases of the fully connected layer respectively;

[0271] In this step, the train mileage prediction network model is constructed to continuously len The characteristic parameter sequence of the positioning solution cycle is used as the input of the train mileage prediction network, and the train mileage of the next positioning solution cycle is used as the output. The expression of the prediction model is: Where: ΔS′ t+1 is the train mileage within the next positioning solution cycle ΔT output by the model at time t+1, f(·) is the prediction model of the train mileage prediction network, V t is the train state characteristic parameter sequence input to the model at time t;

[0272] The feature sequence is input into the prediction model f(·). The model extracts and calculates features based on the input sequence, and calculates the changing trend of the train driving status layer by layer. Through model training, the relationship between the feature parameter sequence and the mileage is learned, thereby generating the train mileage ΔS′ for the next positioning solution cycle ΔT t+1 The specific process includes the following steps:

[0273] (1) Let the input sequence V = [V1, V2, V3, ..., V t ]

[0274] (2) The input sequence V undergoes a nonlinear transformation to generate a hidden state sequence H (1)

[0275]

[0276] (3) Using the first hidden state H (1) As input, after the second layer transformation, a new hidden state sequence H is obtained. (2)

[0277]

[0278] (4) According to Q = XW Q , K = XW K ,P=XW P Generate Query, Key and Value matrices, where W Q , W K , W P is a trainable weight matrix; then calculate the dynamic weight and weight A,

[0279]

[0280] The softmax function is used to standardize the weight value to the [0,1] interval;

[0281] (5) Perform weighted summation on the Value matrix P according to the dynamic weight and weight A to obtain the weighted aggregation output H agg :

[0282] H agg =AP

[0283] The eigenvector H agg Input into the fully connected layer to generate the predicted mileage ΔS′ of the train t+1

[0284] ΔS′ t+1 =W o H agg +b o

[0285] Where W o and b o are the weights and biases of the fully connected layer respectively;

[0286] (6) Use mean square error (MSE) as the loss function to calculate the error between the predicted mileage and the actual mileage:

[0287]

[0288] Where N is the number of samples, ΔS t+1 is the actual running mileage of the train;

[0289] For the train mileage prediction network model that has reached convergence, select multiple train operation data as the test set, adjust the model parameters according to the model prediction accuracy, and achieve model optimization;

[0290] Step S43: Real-time acquisition of train status data during the train operation. len Construct train state characteristic parameter sequence V t Whenever new data arrives, add the new data to the feature sequence and remove the earliest data, keeping the window length W len , with the updated feature sequence As the latest input of the train mileage prediction network model, the train predicted mileage ΔS′ in the next positioning solution cycle t+1 As output, a forward prediction of the train operation status is performed.

[0291] In this step, the train status data includes the current train position x t , train speed v t , train acceleration a t , Train mileage ΔS t And the train's cumulative mileage S t , the train state characteristic parameter sequence V t Including the current train position x t , train speed v t , train acceleration a t And the train's cumulative mileage S t , take the average speed at each moment in the sliding window and the mean acceleration Composition sequence;

[0292] Take the maximum value of the speed and acceleration sequence at each moment in the sliding window to form the maximum speed sequence v of the train operation data t,max and the acceleration maximum sequence a t,max , calculate the standard deviation v of the two sequences respectively t,std 、a t,std , extreme value v t,range 、a t,range And the ratio of the maximum value to the minimum value v t,rate 、a t,rate ;

[0293] Whenever new data arrives, add the new data to the feature sequence and remove the earliest data, keeping the window length W len, with the updated feature sequence As the latest input of the train mileage prediction network model, the predicted mileage value ΔS′ of the next positioning solution cycle is output in real time t+1 , to realize the forward prediction of the train’s operating status.

[0294] The step S5 specifically includes the following steps:

[0295] Step S51: according to the current train running direction D(t), extract the virtual transponder identity sequence in the virtual transponder basic database to construct the virtual transponder real-time state sequence, and determine the virtual transponder number b closest to the target to be captured in this direction. i ;

[0296] In this step, the virtual transponder real-time status sequence mainly describes the interval from the starting station A to the terminal station B and all N AB The state information data of the virtual transponder is obtained by capturing the state Acq(i,D N ), where i represents the virtual transponder number, i = 1, 2, ..., N AB , D N It is used to mark the actual running direction of the train in the current balise capture process, Acq(i,D N ) takes a value of 0, indicating that the virtual transponder is not captured; Acq(i,D N ) takes a value of 1, indicating that the virtual balise has been captured. During the initialization phase before the train runs on the target line, the capture status of all virtual balises must be reset to zero.

[0297] Step S52: according to the number b of the nearest target virtual transponder to be captured, i Extract the corresponding virtual transponder spatial data and determine the target virtual transponder mileage information When the predicted running mileage of the next positioning solution cycle of the train calculated at time t meets the following conditions, the current time t is determined as the pre-capture trigger time of the target virtual balise to be captured

[0298] ΔS′ t+1 ≥ΔS NVB

[0299] Among them, ΔS NVB is the distance between the train and the target virtual balise at the current moment;

[0300] Figure 4This is a schematic diagram of the principle of predicting the pre-capture triggering moment of the target virtual transponder to be captured in this embodiment. This figure intuitively shows the comparison process of the predicted mileage and the target virtual transponder position at different time steps to determine the optimal pre-capture triggering moment.

[0301] Step S53, within the unit positioning solution cycle ΔT, multi-step forward prediction is performed at fixed time intervals δt, and the train running mileage S′(t+kδt), k=1,2,...,N-1, Compute the predicted position of the train at each time step.

[0302] In this step, the multi-step forward prediction of train mileage S'(t+kδt) is performed at the time interval δt, and an input feature sequence is constructed according to the current train state and historical state. The feature sequence is used as the initial input of the model for the first forward prediction. The model outputs the predicted mileage S'(t+δt) of the first time step, which represents the mileage of the train within the time interval δt. Based on the prediction of the first time step, the prediction result is recursively used as the input of the next time step to continue the multi-step forward prediction. The specific process is as follows:

[0303] 1) Add the predicted mileage S′(t+δt) of the first time step to the feature sequence and use Estimate the velocity eigenvector for the next time step, Estimate the acceleration eigenvector of the next time step, and calculate the mathematical and statistical eigenvalues ​​of the train state feature sequence of the next time step based on it, and update the input feature sequence to include the latest prediction information;

[0304] 2) Forward prediction again: input the updated input feature sequence into the model to obtain the predicted mileage S′(t+2δt) for the next time step;

[0305] 3) Repeat the above process: continue to recursively update the input feature sequence and input it into the model for prediction until the prediction of all steps is completed;

[0306] In each recursion, the model outputs the mileage of the next time step, and finally obtains the predicted mileage sequence {S′(t+δt),S′(t+2δt),...,S′(t+(N-1)δt)} for multiple time steps in the future

[0307] Step S54, calculate the train running mileage S'(t+kδt) and the target virtual balise mileage at each time step The distance ΔS k (ΔS k=|S′(t+kδt)-S(t)|), among all the predicted moments, find the predicted train position closest to the target virtual balise position, i.e., ΔS k The minimum time t+kδt, ​​the target virtual transponder capture time difference kδt is obtained;

[0308] In this step, for each prediction time kδt, the distance between the predicted train mileage and the target virtual balise is calculated:

[0309] ΔS k =|S′(t+kδt)-S(t)|

[0310] At all N predicted moments, traverse the calculated distances, find the minimum value, and determine the moment corresponding to the minimum distance:

[0311] k min =argmin(S′(t+δt),S′(t+2δt),...,S′(t+(N-1)δt))

[0312] Step S55, determine that the time t+kδt is numbered b i The capture time of the nearest target virtual transponder to be captured, that is, T cap =t+kδt.

[0313] Figure 5 It is a schematic diagram of the principle of predicting the capture decision of the nearest target virtual transponder to be captured in this embodiment. Figure 4 In the method, the mileage of the train is accumulated through multi-step forward prediction, and the distance between the train and the nearest virtual balise to be captured is calculated in each time step to determine the capture time;

[0314] Figure 6 FIG. 1 is a schematic diagram of the capture process and capture error of the virtual transponder in this embodiment. Figure 5 As shown in the figure, 50 virtual balises are arranged at equal intervals of 1.5 km, and the train runs along the straight track at a speed of 80 km / h. The eastward position of the virtual balise extends from 500 meters to 2000 meters, and the northward position gradually decreases from 2000 meters to 1700 meters. The capture system predicts the train position near each virtual balise, the black line represents the train track, the triangle represents the virtual balise position, and the diamond is the capture position. Due to measurement errors, there is a certain deviation between the capture position and the virtual balise position. The bar chart shows the capture error of the target virtual balise to be captured.

[0315] The step S6 specifically includes the following steps:

[0316] Step S61: extract the three-dimensional coordinate position of the captured virtual transponder that is closest to the target to be captured mileage Provides the current position correction information to the position calculation process;

[0317] Step S62: at the capture time of the nearest target virtual transponder to be captured, update the real-time state sequence of the virtual transponder and modify the capture state Acq(b i ,D N ) is captured;

[0318] Step S63: along the actual running direction of the train, select the next virtual balise in front of the train as the nearest target virtual balise to be captured in the subsequent running process of the system, and update the number of the nearest target virtual balise to be captured to b. i+1 .

[0319] Figure 7 Schematic diagram of a capture scheme according to an embodiment of the present invention, wherein the grey portion shows the difference between the capture scheme designed according to the embodiment of the present invention and the traditional capture scheme.

[0320] In summary, the method of the present invention combines ultra-wideband technology with Beidou positioning, avoiding the problem of difficult balance between capture accuracy and missed capture risk in conventional capture schemes from the source, ensuring continuous and stable high-precision positioning in complex environments regardless of whether Beidou signals are available, eliminating the availability limitations of virtual transponder capture, and effectively improving the capture accuracy and stability of virtual transponders in complex environments.

[0321] The present invention fully considers the variability of Beidou observation conditions during train operation, and enhances Beidou by introducing ultra-wideband technology. It not only replaces Beidou positioning in tunnels and other environments where Beidou signals are unstable or blocked, but also reinforces Beidou signals in interfered areas, providing continuous positioning results. By constructing a mileage prediction model, it changes the limitation of relying on a fixed capture radius in conventional capture schemes, so that capture judgment no longer relies solely on real-time positioning solution results, reduces the impact of Beidou signal fluctuations, ensures the train's accurate capture of the target virtual transponder, and provides higher protection for continuous positioning and safety monitoring of on-board equipment.

[0322] The ultra-wideband technology in the embodiments of the present invention can effectively ensure that the implementation of the virtual balise technology has a continuous and reliable source of basic information. By constructing a train mileage prediction network model, the problem of balancing capture accuracy and missed capture risk in conventional capture schemes can be avoided, thereby achieving seamless capture of the virtual balise.

[0323] The virtual transponder capture method based on the ultra-wideband enhanced global navigation Beidou system provided in this embodiment improves the limitations of the traditional method that solely relies on Beidou signal positioning in the virtual transponder capture judgment process. The method uses the train mileage prediction as the basis for judgment during the capture process, constructs a train running mileage prediction model, and significantly improves the timeliness and accuracy of the capture judgment through the effective use of the train's historical status data. The problem of the difficulty in achieving both positioning accuracy and the risk of missed capture in the traditional method that relies on a fixed capture space radius is overcome. The method is suitable for various Beidou signal observation conditions while appropriately controlling the amount of calculation and has strong adaptability.

[0324] The method provided in this embodiment makes full use of the information support of the virtual transponder basic database, and introduces the regional enhancement mechanism provided by ultra-wideband positioning under special conditions such as specific Beidou service restrictions and Beidou positioning performance degradation. It will be able to effectively ensure that the implementation of virtual transponder technology has a continuous and reliable source of basic information, and thus provide support for the seamless optimization of virtual transponder functions. It has broad application prospects in railway applications such as train dynamic status monitoring and train tracking and early warning.

[0325] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0326] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0327] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0328] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A train positioning method based on ultra-wideband enhanced Beidou and virtual transponder, characterized in that: include: Establish a virtual balise basic database and an ultra-wideband device basic database based on the collected data of the target track line; Establish a basic database of positioning observation quality based on the Beidou positioning observation quality of the target track line, and compile an ultra-wideband / Beidou joint positioning strategy set based on prior terrain environment information, Beidou positioning observation quality exploration information, and historical train operation data along the line; During the operation of the train, a positioning strategy is dynamically selected in the ultra-wideband / Beidou joint positioning strategy set according to the real-time observation conditions of the Beidou signal, the three-dimensional spatial position of the current train is calculated, and multiple ultra-wideband base station observation data sets are extracted according to the current running mileage of the train and the ultra-wideband device spatial data in the ultra-wideband device basic database to obtain the ultra-wideband positioning solution data source of the train; Constructing a train state characteristic parameter sequence, establishing a train mileage prediction network model, inputting the train state characteristic parameter sequence during the real-time operation of the train into the train mileage prediction network model, and obtaining the train mileage information at the next moment; Extracting information related to the current train and the mileage information of the train at the next moment from the virtual balise basic database, determining the train mileage and pre-capture triggering moment of the next positioning solution cycle, calculating the predicted position of the train closest to the nearest target virtual balise from the pre-capture triggering moment, estimating the capture time difference of the nearest target virtual balise, and determining the capture moment of the nearest target virtual balise to be captured; The position correction information is sent at the capture moment of the nearest target virtual balise to be captured, and the train corrects the current position according to the position correction information, while updating the real-time state sequence of the virtual balise.

2. The train positioning method based on ultra-wideband enhanced Beidou and virtual balise according to claim 1 is characterized in that: The method of establishing a virtual balise basic database and an ultra-wideband device basic database based on the collected target track line data includes: Set the target operation route from the starting station A to the terminal station B, and its mileage range is from the starting station center mileage S A Mileage to terminal station center S B , build a basic database of virtual transponders and ultra-wideband equipment covering the entire mileage range of the line; The virtual transponder basic database includes track line spatial data, virtual transponder identity sequence and virtual transponder spatial data. The track line spatial data describes the spatial data of the track line between the starting station A and the terminal station B. Combined with the actual positioning measurement, data processing and format conversion of several key points on each track line in the interval and station, a complete track line spatial database is formed according to a specific data item format. The database stores the spatial information and attribute information of all key points of the line. index,λ The spatial data items stored at the key points of the route include: the key point mileage S (P index,λ ), the three-dimensional coordinate position of the key points of the line (L index,λ ,B index,λ ,H index,λ ), the stored attribute data items include: running direction D (P index,λ ), terrain environment attributes U(P index,λ ), the subscript index indicates the key point number of the route, the subscript λ indicates the route number, and the key point mileage S(P index ) is directly related to the uplink and downlink directions of the line. The mileage in the uplink direction decreases, while the mileage in the downlink direction increases. For any two adjacent key points of the line, the numbers are P α , P α+1 , the following conditions are met: dis{P α (L α ,B α ,H α ),P α+1 (L α+1 ,B α+1 ,H α+1 )}-|S(P α )-S(P α+1 )|≤h s Where dis{*} represents the straight-line distance solution function between two points based on three-dimensional space coordinates, h S is the tolerable orbital space data error limit; The virtual transponder identity sequence describes the interval from the origin station A to the terminal station B and all N AB The identity data of the virtual transponder includes: target line index λ, direction flag D N and virtual transponder number b i ; The virtual transponder space data items include: virtual transponder number b i , Virtual transponder mileage and the three-dimensional coordinate position of the virtual transponder The ultra-wideband basic database contains ultra-wideband equipment spatial data, specifically including ultra-wideband base station number A j 、Ultra-wideband base station mileage and the three-dimensional coordinates of the ultra-wideband base station 3. The train positioning method based on ultra-wideband enhanced Beidou and virtual balise according to claim 1 is characterized in that: The method of establishing a basic positioning observation quality database based on the Beidou positioning observation quality of the target track line and compiling an ultra-wideband / Beidou joint positioning strategy set based on prior terrain environment information, Beidou positioning observation quality exploration information, and historical train operation data along the line includes: The historical driving data along the target track line is used to explore the quality of Beidou positioning observations, and a basic positioning observation quality database is established to store Beidou positioning quality observation data, including: Beidou satellite signal-to-noise ratio mean SNR avg (P index,λ ), the number of visible BeiDou satellites n (P index,λ ), position precision dilution PDOP (P index,λ ), average pseudorange residual e P,avg (P index,λ ), Beidou satellite signal-to-noise ratio standard deviation σ SNR (P index,λ ) and the mean SNR of the Beidou satellite signal-to-noise ratio drop (P index,λ ); Based on the Beidou positioning observation quality exploration information and the historical train operation data of multiple lines, the possible Beidou observation quality conditions of the target track line are analyzed, including three categories: normal Beidou signal quality, degraded Beidou signal quality, and Beidou signal failure; The positioning strategy content Method contained in the ultra-wideband / Beidou joint positioning strategy set is determined according to the common Beidou observation quality of the line. The positioning strategy content Method includes three types: ultra-wideband independent positioning (UWB), Beidou independent positioning (GNSS), and ultra-wideband / Beidou joint positioning (UWB / GNSS).

4. The train positioning method based on ultra-wideband enhanced Beidou and virtual balise according to claim 3 is characterized in that: The method of dynamically selecting a positioning strategy in the UWB / Beidou joint positioning strategy set according to the real-time observation conditions of Beidou signals during the operation of the train to calculate the three-dimensional spatial position of the current train operation includes: During the real-time operation of the train, at time t, the train control onboard equipment receives the signal from the Beidou positioning receiver and processes it to obtain the current navigation Beidou ephemeris and the pseudo-ranges of n visible Beidou satellites {ρ1(t), ρ2(t), …, ρ n (t)} real-time observation data, using the extracted spatial data of m UWB devices to obtain the relative distance information between the device and the current train {d1(t), d2(t), …, d m (t)}; In each positioning solution cycle, the system determines whether the Beidou signal meets the availability conditions: SNR avg ≥SNR min And n≥n min Determine the signal quality status of the Beidou signal that meets the availability conditions, and dynamically select a positioning strategy in the UWB / Beidou joint positioning strategy set based on the signal quality status of the Beidou signal A) Signal quality is normal PDOP≤PDOP max And e p,avg ≤e p,max and If the conditions are met, the Beidou independent positioning strategy is selected; B) Signal quality degradation due to occlusion PDOP > PDOP max or e p,avg > e p,max If the conditions are met, the UWB / Beidou joint positioning strategy is selected; C) Signal quality degradation due to interference or SNR drop > SNR drop,max If the conditions are met, the UWB / Beidou joint positioning strategy is selected in real time; If the system determines that the Beidou signal does not meet the availability conditions, the ultra-wideband independent positioning strategy is selected; Construct the measurement vector z according to the selected positioning strategy and real-time observation information t The train's three-dimensional position {X(t), Y(t), Z(t)} and the clock error Δt constitute the state vector to be estimated, and the nonlinear state filter is used to calculate the estimated value of the state vector Get the 3D position of the train 5. The train positioning method based on ultra-wideband enhanced Beidou and virtual balise according to claim 4 is characterized in that: The method of extracting multiple ultra-wideband base station observation data sets according to the current running mileage of the train and the ultra-wideband device spatial data in the ultra-wideband device basic database to obtain the ultra-wideband positioning solution data source of the train includes: When the Beidou signal quality is normal, the Beidou independent positioning strategy is used to construct the measurement vector as follows: z t =[ρ1(t),ρ2(t),…,ρ n (t)] T When the BeiDou signal is still available but the quality is degraded, the UWB / BeiDou joint positioning strategy is used to construct the measurement vector: z t =[ρ1(t),ρ2(t),…,ρ n (t),d1(t),d2(t),…,d m (t)] T When the Beidou signal fails, the ultra-wideband independent positioning strategy is used to construct the measurement vector: z t =[d1(t),d2(t),…,d m (t)] T The state vector to be estimated is composed of the three-dimensional position of the train {X(t), Y(t), Z(t)} and the clock error Δt: x t =[X(t),Y(t),Z(t),Δt] T Combined with the three-dimensional coordinate positions of each visible Beidou satellite {X i (t),Y i (t),Z i (t)} Write the pseudo-range observation model based on the simplified three-dimensional coordinate position of the connected ultra-wideband device Write down the range-finding model and use the nonlinear Kalman filter to calculate the state vector estimate State vector estimate The specific process includes the following steps: P t =(I-K t H t )P t,t-1 in, P t,t-1 are the state vector prediction value and the one-step prediction variance matrix, respectively. t,t-1 is the state transfer matrix, K t is the equivalent filter gain matrix, Q t , R t are the variance matrices of system error and measurement error, respectively, t is the linearized equivalent measurement matrix obtained by transforming the observation model, P t is the state estimation variance matrix; Set up dynamic iterative optimization judgment logic, and use the current train operation information and observation information at time t to iteratively optimize and obtain the optimal solution for train position estimation Estimate the optimal train position Convert to coordinate position Get the track key point P closest to the current position of the train index,λ , determine the train's running direction and line number; The state vector estimate obtained by Kalman filtering is used as the initial state of the dynamic iterative optimization judgment logic Set the optimization objective function X * To minimize the residual sum of squares, according to the three different strategies in the selected ultra-wideband / Beidou joint positioning, three optimization objective functions are specifically corresponding to each other: Where: e UWB,j =d j -||xp UWB,j || is the ranging residual of the jth ultra-wideband base station, d j is the actual measured ultra-wideband ranging distance, x is the train position state to be optimized, and p UWB,j are the location coordinates of the known ultra-wideband base station; is the pseudorange residual of the i-th Beidou satellite, is the Doppler velocity residual; and is the corresponding covariance matrix; x GNSS and x UWB is the train position state to be optimized calculated by Beidou and UWB, e Fus is the Euclidean distance difference between Beidou and UWB positioning results, e Fus =||x GNSS -x UWB ||, ∑Fus is the covariance of the fusion residual; Use the nonlinear least squares method to perform state iterative optimization to determine whether the optimization accuracy is met: ||and(x (k) )-former (k-1) )||<ε r In the formula, ε r is the maximum acceptable variation of the residual error. If the above formula is satisfied, the optimal solution of the optimized train position estimation is output. If the above formula is not satisfied and the maximum number of iterations has been reached, the iteration is stopped and the current optimal estimation result is output; Depend on Iteratively solve the corresponding geographic coordinates of the train Based on the current coordinate position of the train Determine the track key point P closest to the current position of the train index,λ , thereby determining the train's running direction D(t) and the line number λ; Based on the train running status, the track line spatial data in the virtual balise basic database is extracted, and the optimal train position is estimated based on the calculated Use the nearest neighbor principle to find the segment between key points of the track closest to the optimal position estimate in the track spatial data Calculate the current running mileage of the train S(t): S(t)=S(P F )±γ||P F -P E || Where S(P F ) is the key point P of the line F Mileage, γ is the projection coefficient, ||P F -P E || is a line segment length; At the time t when the nearest virtual transponder to be captured is captured VB , use the correction information provided by the nearest virtual balise to be captured to update the current position of the train, and replace the estimated coordinates of the current optimal position of the train with the three-dimensional coordinate position of the nearest virtual balise to be captured Update mileage at the same time Implement correction information integration in the position calculation process; According to the current mileage S(t) of the train and the spatial data of the ultra-wideband equipment in the basic database of ultra-wideband equipment, multiple ultra-wideband base station observation data sets are extracted according to the distance principle to obtain the ultra-wideband positioning solution data source of the train.

6. The train positioning method based on ultra-wideband enhanced Beidou and virtual balise according to claim 5 is characterized in that: The method of constructing a train state characteristic parameter sequence, establishing a train mileage prediction network model, inputting the train state characteristic parameter sequence during the real-time operation of the train into the train mileage prediction network model, and obtaining the train mileage information at the next moment includes: Using the length W len The sliding window extracts the mathematical and statistical characteristics of train operation data from the historical driving data along the target track line, and constructs the train state characteristic parameter sequence V t , establish a sample set containing time series information, where each sample contains sequence data of train state characteristic parameters, and the mathematical statistical characteristics of the train operation data include the current train position x t , train speed v t , train acceleration a t , Train mileage ΔS t , the train's cumulative mileage S t , mean train speed Mean train acceleration Train speed maximum sequence v t,max , Train acceleration maximum sequence a t,max And the standard deviation v of the velocity and acceleration extreme value series t,std 、a t,std , extreme value v t,range 、a t,range And the ratio of the maximum value to the minimum value v t,rate 、a t,rate ; Then the train state characteristic parameter sequence is: The train state characteristic parameter sequence is normalized by the min-max normalization method to obtain a continuous W len The characteristic parameter sequence of the positioning solution cycle is used as the input of the train mileage prediction network model, and the predicted train mileage in the next positioning solution cycle is used as the output. The characteristic sequence is input into the train mileage prediction network model f(·). The model extracts and calculates the features according to the input sequence, and calculates the change trend of the train driving state layer by layer. Through the training of the model, the relationship between the characteristic parameter sequence and the mileage is learned, so as to generate the mileage information ΔS′ of the train in the next positioning solution cycle ΔT. t+1 The specific process includes the following steps: (1) Let the input sequence V = [V1, V2, V3, ..., V t ] (2) The input sequence V undergoes a nonlinear transformation to generate a hidden state sequence H (1) (3) Using the first hidden state H (1) As input, after the second layer transformation, a new hidden state sequence H is obtained. (2) (4) According to Q = XW Q , K = XW K ,P=XW P Generate Query, Key and Value matrices, where W Q , W K , W P is a trainable weight matrix; then calculate the dynamic weight and weight A, The softmax function is used to standardize the weight value to the [0,1] interval; (5) Perform weighted summation on the Value matrix P according to the dynamic weight and weight A to obtain the weighted aggregation output H agg : H=AP agg The eigenvector H agg Input into the fully connected layer to generate the predicted mileage ΔS of the train t ' +1 ΔS t ′ +1 =W o H agg +b o Where W o and b o are the weights and biases of the fully connected layer respectively; (6) Use mean square error (MSE) as the loss function to calculate the error between the predicted mileage and the actual mileage: Where N is the number of samples, ΔS t+1 is the actual running mileage of the train; Select mean square error As the target loss function, where N is the number of samples, ΔS t+1 Based on the actual running mileage of the train, the model is trained iteratively multiple times to improve its prediction ability of the train running status until the model converges; For the train mileage prediction network model obtained through training after convergence, multiple train historical operation data are selected as the test set, and the model parameters are adjusted according to the model prediction accuracy to achieve model optimization. The specific structure of the train mileage prediction network model is as follows: V=[V1,V2,V3,...,V t ] Q=XW Q ,K=XW K ,P=XW P H=AP agg ΔS t ′ +1 =W o H agg +b o Among them, f (1) is the activation function of the first layer, θ (1) is the weight and bias set of the first layer network, f (2) is the activation function of the second layer, θ (2) is the weight and bias set of the second layer network, W Q , W K , W P is a trainable weight matrix, A is a dynamic weighted sum weight, W o and b o are the weight and bias of the fully connected layer respectively.

7. The train positioning method based on ultra-wideband enhanced Beidou and virtual balise according to claim 6 is characterized in that: The extracting of information related to the current train and the mileage information of the train at the next moment from the virtual transponder basic database, determining the train mileage and pre-capture triggering moment of the next positioning solution cycle, calculating the predicted position of the train closest to the nearest target virtual transponder from the pre-capture triggering moment, estimating the capture time difference of the nearest target virtual transponder, and determining the capture moment of the nearest target virtual transponder to be captured, includes: According to the current train running direction D(t), extract the virtual balise identity sequence in the virtual balise basic database to construct the virtual balise real-time state sequence, and determine the virtual balise number b closest to the target to be captured in this direction i The virtual transponder real-time status sequence describes the interval from the starting station A to the terminal station B and all N contained in the station AB The state information data of the virtual transponder is obtained by capturing the state Acq(i,D N ), where i represents the virtual transponder number, i = 1, 2, ..., N AB , D N It is used to mark the actual running direction of the train in the current balise capture process, Acq(i,D N ) takes a value of 0, indicating that the virtual transponder is not captured; Acq(i,D N ) takes a value of 1, indicating that the virtual transponder has been captured; According to the number b of the nearest target virtual transponder to be captured i Extract the corresponding virtual transponder spatial data and determine the target virtual transponder mileage information When the predicted running mileage of the next positioning solution cycle of the train calculated at time t meets the following conditions, the current time t is determined as the pre-capture trigger time of the target virtual balise to be captured ΔS t ′ +1 ≥ΔS NVB Among them, ΔS NVB is the distance between the train and the target virtual balise at the current moment; In the unit positioning solution cycle ΔT, multi-step forward prediction is performed at fixed time intervals δt, and the train mileage S′(t+kδt), k=1,2,...,N-1, Compute the predicted position of the train at each time step. The train mileage S′(t+kδt) is predicted in multiple steps forward at a time interval δt. The input feature sequence is constructed according to the current train status and historical status. The feature sequence is used as the initial input of the model for the first forward prediction. The model outputs the predicted mileage S′(t+δt) of the first time step, which represents the mileage of the train within the time interval δt. Based on the prediction of the first time step, the prediction result is recursively used as the input of the next time step to continue the multi-step forward prediction. In each recursion, the model outputs the mileage of the next time step, and the predicted mileage sequence of multiple future time steps is obtained {S′(t+δt), S′(t+2δt), ..., S′(t+(N-1)δt)}. Calculate the train running mileage S′(t+kδt) and the target virtual balise mileage at each time step The distance ΔS k (ΔS k =|S′(t+kδt)-S(t)|), among all the predicted moments, find the predicted train position closest to the target virtual balise position, i.e., ΔS k The minimum time t+kδt, ​​the target virtual transponder capture time difference kδt is obtained; The judgment time t+kδt is numbered b i The capture time T of the nearest virtual transponder to be captured cap , that is, T cap =t+kδt.

8. The train positioning method based on ultra-wideband enhanced Beidou and virtual balise according to claim 7 is characterized in that: The sending of position correction information at the capture time of the nearest target virtual balise to be captured, the train correcting the current position according to the position correction information, and updating the real-time state sequence of the virtual balise at the same time, comprises: Extract the three-dimensional coordinate position of the captured virtual transponder closest to the target to be captured and mileage Get the train's current position correction information; At the capture time of the nearest target virtual transponder to be captured, the real-time state sequence of the virtual transponder is updated, and the capture state Acq(b i ,D N ) is captured; Along the actual running direction of the train, select the next virtual balise closest to the front as the closest target virtual balise to be captured in the subsequent running process of the system, and update the number of the closest target virtual balise to be captured to b i+1 .

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