Train positioning method based on fusion of on-board IMU and track geometric feature data

Abstract

The present invention discloses a train positioning method based on the fusion of on-board IMU and track geometry feature data, belonging to the field of train positioning technology; the method comprises: pre-processing the measurement data collected by a single on-board IMU, dividing the line into sections, and obtaining the on-board IMU measurement data of each section; performing on-board IMU autonomous navigation calculation, and calculating the initial running speed and mileage of the train in the section through the autonomous navigation calculation model of the on-board IMU; performing map matching based on the curvature information of the track geometry and the information calculated by the on-board IMU autonomous navigation; fusing the on-board IMU measurement information with the slope and curvature information of the track geometry; calibrating the train running speed and updating the train position. The present invention adopts the output information of a single on-board IMU to fuse with the slope and curvature information of the track geometry to realize the calibration of the train running speed, and further realizes the precise positioning of the train.

Description

Technical Field

[0001] The present invention relates to the technical field of underground positioning of subway trains, and in particular to a train positioning method based on the fusion of on-board IMU and track geometric feature data. Background Art

[0002] Urban rail transit train positioning involves determining the train's absolute mileage position on the track. This position is the distance along the track from the starting point to the train's current position. Therefore, the urban rail transit positioning problem can be viewed as a one-dimensional positioning problem along the track.

[0003] Onboard IMUs are commonly used to assist train positioning systems. IMU sensors generally consist of a three-axis accelerometer and a three-axis gyroscope. This sensor is affected by its own random walk, resulting in cumulative errors in the train's estimated attitude, speed, and distance traveled. Currently, the method for using onboard IMUs for train positioning is typically based on map matching technology based on track curvature. Track curvature information is calculated based on the angular velocity output by the IMU and the speed information measured by the speed sensor, and then matched with the track design curvature map, usually using the start and end points of the curve as matching information. Currently, map matching technology has two disadvantages: 1) Speed ​​information relies on other speed sensors, such as the speed information of the train control system, which is difficult for non-invasive positioning systems to obtain; 2) It fails to reduce the cumulative error of the train's movement between two adjacent matching positions. This error gradually increases with the distance the train travels, thereby affecting the accuracy of the train's position calculation.

[0004] Furthermore, without map matching information, using only an IMU sensor can be used to calculate the distance traveled by a train within a station section, using the section length as the true value for evaluation. A drawback of this method is that the rail mileage information at the location of the IMU sensor is typically unknown. During train operation, this method can only be used to estimate the relative distance traveled by the train; the algorithm cannot be used to estimate the train's track mileage position. Therefore, precise underground positioning technology for urban rail transit trains based on a single onboard IMU has not yet been achieved. Summary of the Invention

[0005] In view of this, the present invention provides a train positioning method based on the fusion of on-board IMU and track geometry feature data, which can solve the above-mentioned technical problems. The output information of a single on-board IMU is integrated with the slope and curvature information of the track geometry to realize the calibration of the train running speed, further realize the precise positioning of the train, and improve the accuracy of train positioning.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] In a first aspect, an embodiment of the present invention provides a train positioning method based on the fusion of on-board IMU and track geometric feature data, the method comprising the following steps:

[0008] S1. Preprocess the measurement data collected by a single on-board IMU, divide the entire route into sections, and obtain the on-board IMU measurement data for each section;

[0009] S2. Perform autonomous navigation dead reckoning using the onboard IMU, and calculate the train's initial operating speed and mileage within the section using the onboard IMU's autonomous navigation dead reckoning model.

[0010] S3, map matching is performed based on the curvature information of the track geometry and the information calculated by the onboard IMU autonomous navigation;

[0011] S4. Based on map matching, the onboard IMU measurement data is integrated with the slope and curvature information of the track geometry; the train speed is calibrated and the train position is updated.

[0012] Furthermore, in step S2, the IMU autonomous navigation calculation process includes two states: train stop and train running, where:

[0013] When the train stops at a station, the train's posture is updated and used as the initial posture for the next section. The train's posture and mileage are calculated during operation. The initial posture calculation model is:

[0014]

[0015] Among them, φ int ,θ int , They are the nodding, rolling and shaking Euler angles describing the posture of the train when it stops at the station; are the average accelerations of the x, y, and z axes output by the IMU when the train stops; g is gravity;

[0016] When the train is running, the IMU measurement data is used to calculate the train's attitude, speed and travel distance;

[0017] The acceleration in the direction of train movement is:

[0018]

[0019] in, is the acceleration of the train in the direction of travel, represents the longitudinal acceleration of the train at time t, φ t is the Euler angle of the train at time t;

[0020] Based on the quaternion, the posture of the train is updated during operation, and the updated model is:

[0021]

[0022] Among them, Q t is the quaternion describing the posture of the train at time t, Represents the quaternion Q t The derivative of They represent the pitch angular velocity, roll angular velocity and yaw angular velocity of the train at time t respectively;

[0023] The model for IMU autonomous navigation to calculate train posture, speed and travel distance is:

[0024]

[0025] Among them, s t represents the distance traveled by the train at time t, is the speed of the train at time t, I represents the operator that converts the attitude quaternion into the acceleration of gravity in the direction of train movement; 4×4 represents the 4th-order identity matrix.

[0026] Furthermore, in step S3, the map matching process includes extracting key points for measuring curvature, specifically, calculating the curvature value using the train speed inferred from the IMU autonomous navigation and the measured angular velocity information. The calculation method is:

[0027]

[0028] Among them, κ t represents the measured curvature value at time t;

[0029] The key points of the track curve are the spatial sequence information with mileage as the coordinate, which corresponds to the characteristics of the time series data measured by IMU as the points where the measured curvature changes from zero or changes to zero.

[0030] Furthermore, in step S4, the curvature of the circular curve in the track plane and the angular velocity of the train when passing through the circular curve are used to calculate the speed. This speed is used as the true value information to calibrate the longitudinal acceleration output by the track slope information and the IMU to calculate the train's travel speed. The specific process includes:

[0031] ① Train speed based on track design slope:

[0032] The train speed is calculated by fusing the designed track slope information with the longitudinal acceleration output by the IMU. The calculation formula is:

[0033]

[0034] in, are the train running acceleration and speed compensated for the track design slope, respectively; is a discrete spatial sampling dataset of orbits; is the spatial value of the rail slope;

[0035] ②Calculate the speed of the train on a circular curve:

[0036] The running speed of the train on the circular curve of the track plane is calculated by fusing the design curvature information of the track and the train angular velocity measured by the IMU. Its expression is:

[0037]

[0038] in, is the running speed of the train on the circular curve, which serves as the true value information of the train speed; is the spatial value of the rail curvature; Represents a set of circular curve space sampling points;

[0039] ③Train speed correction:

[0040] First, within an operating range, the train speed for design slope compensation is expressed as:

[0041]

[0042] in, is the designed gradient compensation speed of the train at t1; the running time series of the train in this interval is expressed as TS = [t1, t2, ..., t j ,…,t N ] T , t j =jΔt; j represents the jth sampling time, and N represents the total number;

[0043] Secondly, the running speed of the train on the curve in the interval is taken as the true value and expressed as:

[0044]

[0045] Where J represents the total number of speed samples of the train on the circular curve in the section, and the time series of the train running on the curve is recorded as T cir =[t′1,t′2,…,t′ j ,…,t′ J ] T ,and

[0046] Finally, the designed slope compensation speed is calibrated using the true value, and the deviation between the true value speed and the designed slope compensation speed at the curve position is recorded as Its expression is:

[0047]

[0048] Based on the speed difference Calculate the difference between all slope compensation and true value in this interval to get the speed difference that needs to be calibrated This information is obtained by the linear interpolation algorithm, and the calculation formula is:

[0049]

[0050] Where interp(·) is a linear interpolation operator;

[0051] Therefore, the corrected train speed is:

[0052]

[0053] in, is the corrected train speed.

[0054] Furthermore, this method uses the calculation of the average positioning error to evaluate the positioning performance of the train, and the formula is:

[0055]

[0056] in, is the average positioning error, I represents the number of key points in the interval, m kpi Indicates the absolute mileage information of the i-th key point in the interval, l i Indicates that the train is in [m kpi ,m kp(i+1) ] within the running distance.

[0057] In a second aspect, an embodiment of the present invention further provides a train positioning system based on the fusion of on-board IMU and track geometric feature data, which applies the above-mentioned train positioning method based on the fusion of on-board IMU and track geometric feature data to perform train positioning. The system includes:

[0058] The data preprocessing module is used to preprocess the measurement data collected by a single on-board IMU, divide the entire route into sections, and obtain the on-board IMU measurement data for each section;

[0059] The IMU navigation calculation module is used to perform on-board IMU autonomous navigation calculation and calculate the initial running speed and mileage of the train in the section through the on-board IMU autonomous navigation calculation model;

[0060] The map matching module is used to perform map matching based on the curvature information of the track geometry and the information calculated by the on-board IMU autonomous navigation;

[0061] The data fusion module is used to fuse the on-board IMU measurement data with the slope and curvature information of the track geometry based on map matching; calibrate the train running speed and update the train position.

[0062] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the above-mentioned train positioning method based on the fusion of on-board IMU and track geometric feature data.

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

[0064] The present invention proposes a train positioning method based on the fusion of on-board IMU and track geometry feature data. The output information of a single on-board IMU is fused with the slope and curvature information of the track geometry to calibrate the train running speed, further realize the precise positioning of the train, and improve the accuracy of train positioning.

[0065] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0066] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0068] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0069] Figure 1 A flow chart of a train positioning method based on the fusion of on-board IMU and track geometry feature data provided in an embodiment of the present invention.

[0070] Figure 2 Schematic diagram of the working principle of the train positioning method based on the fusion of on-board IMU and track geometric feature data provided by an embodiment of the present invention.

[0071] Figure 3 A schematic diagram of the longitudinal acceleration of a train within a single section provided by an embodiment of the present invention.

[0072] Figure 4 A schematic diagram of extracting key point information within an interval provided by an embodiment of the present invention.

[0073] Figure 5 A schematic diagram of the map matching process provided by an embodiment of the present invention.

[0074] Figure 6 A schematic diagram of the experimental equipment layout provided in an embodiment of the present invention.

[0075] Figure 7 A schematic diagram of the longitudinal acceleration measurement results of the IMU in different vehicle compartments provided by an embodiment of the present invention.

[0076] Figure 8 This is a schematic diagram of the yaw (head shaking) angular velocity measurement results of the IMU in different compartments provided by an embodiment of the present invention.

[0077] Figure 9 Schematic diagram of speed-time curves in three typical intervals provided by an embodiment of the present invention.

[0078] Figure 10 Schematic diagram of curvature-mileage curves for three typical intervals provided in an embodiment of the present invention.

[0079] Figure 11 Schematic diagram of error distribution of map matching and data fusion estimation within interval 16 provided by an embodiment of the present invention.

[0080] Figure 12 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0082] In describing the present invention, it should be noted that some processes described in this specification and accompanying drawings include multiple operations that appear in a specific order. However, it should be understood that these operations may be performed in a different order than the order in which they appear, or may be performed in parallel. Furthermore, the use of various sequence numbers is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0083] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0084] See also Figure 1 and Figure 2 As shown, the present invention provides a train positioning method based on the fusion of on-board IMU and track geometric feature data, which mainly includes the following steps:

[0085] S1. Preprocess the measurement data collected by a single on-board IMU, divide the entire route into sections, and obtain the on-board IMU measurement data for each section;

[0086] S2. Perform autonomous navigation dead reckoning using the onboard IMU, and calculate the train's initial operating speed and mileage within the section using the onboard IMU's autonomous navigation dead reckoning model.

[0087] S3, map matching is performed based on the curvature information of the track geometry and the information calculated by the onboard IMU autonomous navigation;

[0088] S4. Based on map matching, the onboard IMU measurement information is integrated with the slope and curvature information of the track geometry; the train speed is calibrated and the train position is updated.

[0089] The specific implementation manner and working principle of the method of the present invention are described in detail below:

[0090] 1. Underground train positioning based on the fusion of a single onboard IMU and track geometry features:

[0091] 1. Data preprocessing:

[0092] The measurement data collected by the onboard IMU is the entire line information from the starting station to the terminal station. The train positioning technology needs to locate the train in the interval between two stations. Therefore, the present invention needs to divide the entire line into intervals and obtain the onboard IMU measurement data of each interval, such as Figure 3 shown.

[0093] 2. IMU autonomous navigation calculation:

[0094] This process mainly uses the IMU's autonomous dead reckoning model to calculate the train's initial speed and mileage within the section. The IMU's autonomous dead reckoning process includes two states: the train is stopped and the train is running.

[0095] When the train stops at a station, the train's posture is updated and used as the initial posture for the train in the next section. The posture and mileage of the train are calculated during the operation. The initial posture calculation model is:

[0096]

[0097] Among them, φ int ,θ int , These are the Euler angles for nodding, rolling, and shaking the head when the train stops at a station; are the average values ​​of the acceleration of the x, y, and z axes output by the IMU when the train stops; g is gravity.

[0098] When the train is running, the IMU output information is used to calculate the train's attitude, speed and travel distance. The acceleration in the train's running direction is:

[0099]

[0100] in, is the acceleration of the train in the direction of travel, represents the longitudinal acceleration of the train at time t, φ t is the Euler angle of the train at time t.

[0101] Based on quaternions, the update model of the train's posture during operation is as follows:

[0102]

[0103] Among them, Q t is the quaternion representing the train's posture at time t, Represents the quaternion Q t The derivative of . They represent the pitch angular velocity, roll angular velocity and yaw angular velocity of the train at time t respectively.

[0104] The model of train attitude, velocity and displacement calculated by IMU navigation is as follows:

[0105]

[0106] Among them, s t represents the distance traveled by the train at time t, is the running speed of the train at time t, I 4×4 represents the 4th-order identity matrix; It represents the operator that converts the attitude quaternion into the acceleration of gravity in the direction of train movement. Its expression is:

[0107]

[0108] in, Indicates H t The transpose of .

[0109] 3. Map matching:

[0110] In this embodiment of the present invention, the core concept of the map matching method is to update the train position by extracting key points of the curve and matching them with the designed track key points. The map matching process includes the definition of the track map, the extraction of key points for measuring curvature, and map matching, where:

[0111] 1) Map definition:

[0112] The track map includes track slope information RSS and plane curve information RCS along the rail mileage, which is recorded as Map = {RSS, RCS}.

[0113] Discrete spatial sampling dataset of orbits It can be expressed as:

[0114]

[0115] Among them, m1 is the starting position of the spatial sampling sequence, and m1+MΔm is the last sampling point of the spatial sampling sequence, that is, the ending position.

[0116] The curvature of the rail is the inverse of the radius of the rail plane curve. This paper uses the curvature information. Therefore, the spatial sequence of curvature and slope along the rail mileage can be expressed as:

[0117]

[0118] in, and are the spatial values ​​of rail slope and curvature respectively.

[0119] 2) Extraction of key points for measuring curvature:

[0120] In this embodiment, the train running speed estimated by the IMU autonomous navigation and the measured angular velocity information are used to calculate the measured curvature value. The calculation method is as follows:

[0121]

[0122] Among them, κ t Represents the measured curvature value at time t.

[0123] The key points of the track curve are the spatial sequence information with mileage as the coordinate, which corresponds to the characteristics of the time series data measured by IMU as the points where the measured curvature changes from zero or changes to zero. Figure 4 Shows the key point extraction information within an interval.

[0124] 3) Map matching:

[0125] In this embodiment, see Figure 5 As shown, Figure 5 The map matching process is shown, where the blue line represents the design track curvature information and the yellow line represents the measured track curvature information. The starting and ending points of the curve are key points. The position information of the key points of the design curvature is used to correct the position information of the key points of the measured curvature. The map matching process maps the time series signal measured by the IMU into a spatial sequence with track mileage as coordinates. This mapping relationship can be expressed as a time-space pair (t,m t ) means that the position of the train at time t is m t .

[0126] 4. Data fusion algorithm:

[0127] In this embodiment of the present invention, based on map matching, the IMU's measurement information is further integrated with the slope and curvature information of the track map. During the map matching process, it can be seen that there is a certain numerical deviation between the calculated curvature information and the designed curvature information, indicating that the speed error estimated by the IMU is large. Therefore, the curvature of the circular curve in the track plane and the angular velocity of the train measured by the IMU are used to calculate the speed of the train when passing through the circular curve. This speed is used as the true value information to calibrate the train's travel speed calculated from the track's slope information and the longitudinal acceleration output by the IMU. The train speed information estimated by this process is calculated as follows.

[0128] ① Train speed based on track design slope:

[0129] According to the IMU's navigation calculations, the train's longitudinal acceleration is related to the longitudinal acceleration measured by the IMU and the train's pitch angle. The train's pitch angle is affected by the track slope. Therefore, the train speed is estimated by fusing the track's designed slope information with the longitudinal acceleration output by the IMU. The calculation is as follows:

[0130]

[0131] in, Train running acceleration and speed compensated for track design grade.

[0132] ②Calculate the speed of the train on a circular curve:

[0133] The train's running speed on a circular curve can be calculated by fusing the track's design curvature information with the train's angular velocity measured by the IMU. This speed is the most likely estimate of the train's actual running speed. The expression is:

[0134]

[0135] in, is the running speed of the train on the circular curve, which serves as the true value information of the train speed; Represents a set of circular curve spatial sampling points.

[0136] ③Train speed correction:

[0137] First, within an operating range, the train speed for design slope compensation is expressed as:

[0138]

[0139] in, is the designed gradient compensation speed of the train at t1. The train operation time series in this interval can be expressed as TS = [t1, t2, ..., t j ,…,t N ] T ,and t j =jΔt, j represents the jth sampling time.

[0140] Secondly, the running speed of the train on the curve in the interval is taken as the true value and can be expressed as:

[0141]

[0142] Where J represents the total number of speed samples of the train on the circular curve in the section, and the time series of the train running on the curve is recorded as T cir =[t′1,t′2,…,t′ j ,…,t′ J ] T ,and

[0143] Finally, the true value is used to calibrate the designed slope compensation speed. The deviation between the true value speed and the designed slope compensation speed at the curve position is recorded as The expression is as follows:

[0144]

[0145] In the embodiment of the present invention, according to the speed difference The difference between all slope compensation and true value in this interval can be further calculated to obtain the speed difference that needs to be calibrated. This information can be obtained by the linear difference algorithm, and the calculation formula is as follows:

[0146]

[0147] Here, interp(·) is used as a linear interpolation operator.

[0148] Therefore, the corrected estimated train speed for:

[0149]

[0150] Based on formula (20), the position of the train in the section can be further calibrated and updated as follows:

[0151]

[0152] in, is the location information of the train at time t after map matching. kp1 Indicates the design mileage location information of the first key point in the interval. This represents the distance traveled by the first key point in the interval relative to the train's departure location. Map matching is used to convert this relative distance into its corresponding absolute track mileage.

[0153] 2. Average positioning error index:

[0154] The goal of the key train positioning problem is to obtain the mileage position of the train on the track in real time. When using on-board IMU sensors, the standard interval length is usually used as the ground truth value for evaluating the train positioning accuracy, where the parking position of the center of the train is assumed to be the center mileage of the station. It can evaluate the positioning performance of the train to a certain extent. However, when the train stops, there is a certain deviation between the center position of the station and the installation position of the IMU. This deviation is unknown and will further reduce the train positioning accuracy. Strictly speaking, the interval length estimation is an estimate of the distance traveled by the train in an interval, but it cannot accurately represent the positioning accuracy of the train. Therefore, in an embodiment of the present invention, the average value of the train positioning error in a shorter interval with the key point as the dividing point is used to evaluate the train positioning accuracy, which is expressed as:

[0155]

[0156] Among them, I represents the number of key points in the interval, m kpi Indicates the absolute mileage information of the i-th key point in the interval, l i Indicates that the train is in [m kpi ,m kp(i+1) ] to calculate the distance traveled.

[0157] The method for evaluating the positioning performance of a train adopted by the present invention is particularly suitable for situations where the true value of the train trajectory cannot be obtained in an underground environment. By using the average positioning error within the interval, the positioning performance of the train in a specific section can be effectively evaluated, avoiding the influence of the randomness of the error distribution on the evaluation results. Since the absolute mileage information of the starting point of the track plane curve is known, it can be used as the true value information to evaluate the positioning performance. This method has several significant advantages: (1) No need for true value trajectory: It is usually very difficult and time-consuming to establish the true value information of the train trajectory in an underground environment, but this method can be evaluated without this information, which greatly simplifies the workload. (2) Efficient and convenient: Using the average positioning error as an evaluation indicator makes the evaluation process of the train positioning performance more efficient and convenient. (3) Engineering application value: From the perspective of track inspection, accurately locating the location of rail defects is crucial for maintenance and inspection tasks. The average positioning accuracy within the interval can reflect the positioning accuracy of the train in a shorter section and has practical engineering application value. Overall, this method provides an effective solution for evaluating the positioning performance of trains in underground environments.

[0158] Furthermore, the present invention verified the applicability of this method through field experiments and evaluated its train positioning accuracy. The results showed that the average positioning error of this method within a certain range was less than 1%. MEMSIMU data installed on different vehicle models was also tested, and the results showed that the system has good reliability. The following is a description of the present invention's experiments:

[0159] This experiment uses a 6-axis MEMSIMU with a sampling frequency of 1000Hz. The device is placed on the ground near the bogie in the carriage. Figure 6 shown.

[0160] Train positioning effect analysis:

[0161] (1) Analysis of vehicle body motion response measured by on-board IMU:

[0162] The train body response to the track geometry measured by the onboard IMU needs to be verified before the train positioning process. After data preprocessing, the longitudinal acceleration and yaw rate measurements measured by the IMU sensors in different compartments are as follows: Figure 7 and Figure 8 The three sets of measured signals show good consistency and repeatability in shape and size, indicating that the train motion response remains stable.

[0163] (2) Train positioning results:

[0164] By using the onboard IMU and rail geometry data fusion algorithm, the cumulative interval length error of the entire line is less than 1.5%. Figure 9 and Figure 10 Figure 2 shows the train speed-time curves and the corresponding estimated curvature for three typical sections. GT speed represents the train speed along the arc and represents the true value, while DF represents the data fusion algorithm. The results show that the measured curvature estimated based on the corrected speed agrees well with the designed track curvature in shape, amplitude, and mileage, demonstrating the suitability of this algorithm for engineering applications.

[0165] (3) Positioning error analysis within the interval:

[0166] The data fusion algorithm proposed in this invention can not only reduce the cumulative error of train positioning in interval length estimation, but also reduce the cumulative error of train positioning in the short track interval between two adjacent key points in the interval. Figure 11 Figure 2 shows the positioning error analysis of the map matching and data fusion algorithms within the interval. The figure shows that the data fusion algorithm significantly reduces the train positioning error within the interval.

[0167] Furthermore, the present invention uses the average positioning error to evaluate the train positioning performance. The average positioning error results of the map matching step and the data fusion step are compared, as shown in Table 1.

[0168] Table 1 Average positioning error of map matching and data fusion algorithms

[0169]

[0170] As can be seen in Table 1, without further DF correction, the average positioning error of map matching can exceed 100 meters in some intervals. This means that in these cases, railway inspectors must walk at least 100 meters to locate track irregularities. This is an inefficient and laborious approach for inspection tasks. However, the data fusion step further reduces the average positioning error to around 20 meters, with an error percentage of less than 1%, which is feasible in engineering applications and meets the practical needs of track inspection.

[0171] From the description of the above embodiments, those skilled in the art will appreciate that the present invention proposes a train positioning method based on the fusion of onboard IMU and track geometry data. This method integrates the output information of a single onboard IMU with the slope and curvature information of the track geometry to calibrate the train's running speed, further enabling precise positioning of the train and improving its accuracy. Furthermore, the present invention proposes estimating the average positioning error to evaluate train positioning performance, providing an effective solution for evaluating train positioning performance in underground environments.

[0172] Furthermore, the present invention also provides a train positioning system based on the fusion of on-board IMU and track geometric feature data, which is applied to the train positioning method based on the fusion of on-board IMU and track geometric feature data described in the above embodiment to achieve accurate train positioning. The system includes:

[0173] The data preprocessing module is used to preprocess the measurement data collected by a single on-board IMU, divide the entire route into sections, and obtain the on-board IMU measurement data for each section;

[0174] The IMU autonomous navigation calculation module is used to perform vehicle-mounted IMU autonomous navigation calculation and calculate the train's initial operating speed and mileage within the section through the vehicle-mounted IMU autonomous navigation calculation model;

[0175] The map matching module performs map matching based on the curvature information of the track geometry and the information calculated by the onboard IMU autonomous navigation;

[0176] The data fusion module is used to fuse the on-board IMU measurement information with the slope and curvature information of the track geometry based on map matching; calibrate the train running speed and update the train position.

[0177] An embodiment of the present invention provides a train positioning system based on the fusion of on-board IMU and track geometric feature data. Its implementation principle and technical effects are the same as those of the aforementioned method embodiment. For the sake of brief description, for parts not mentioned in this embodiment, please refer to the corresponding content in the aforementioned method embodiment, and no further details will be given here.

[0178] Also, see Figure 12 As shown, an embodiment of the present invention further provides an electronic device, which may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and run on the processor 10. The processor executes the computer program to implement a train positioning method based on the fusion of on-board IMU and track geometric feature data in the above method embodiment.

[0179] In some embodiments, the processor 10 may be composed of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes or runs programs or modules stored in the memory 11 and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0180] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, electronic devices, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] It should be noted that the word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present invention can be implemented by means of hardware comprising several distinct components and by means of a suitably programmed computer. The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from the other embodiments. Reference can be made to the same or similar parts of the various embodiments.

[0182] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A train positioning method based on the fusion of on-board IMU and track geometric feature data, characterized in that: The method comprises the following steps: S1. Preprocess the measurement data collected by a single on-board IMU, divide the entire route into sections, and obtain the on-board IMU measurement data for each section; S2. Perform autonomous navigation dead reckoning using the onboard IMU, and calculate the train's initial operating speed and mileage within the section using the onboard IMU's autonomous navigation dead reckoning model. S3, map matching is performed based on the curvature information of the track geometry and the information calculated by the onboard IMU autonomous navigation; S4. Based on map matching, the onboard IMU measurement data is integrated with the slope and curvature information of the track geometry; the train speed is calibrated and the train position is updated; In step S2, the IMU autonomous navigation calculation process includes two states: train stop and train running. When the train stops at a station, the train's posture is updated and used as the initial posture for the next section. The train's posture and mileage are calculated during operation. The initial posture calculation model is: Among them, φ int ,θ int , They are the nodding, rolling and shaking Euler angles describing the posture of the train when it stops at the station; are the average accelerations of the x, y, and z axes output by the IMU when the train stops; g is gravity; When the train is running, the IMU measurement data is used to calculate the train's attitude, speed and travel distance; The acceleration in the direction of train movement is: in, is the acceleration of the train in the direction of travel, represents the longitudinal acceleration of the train at time t, φ t is the Euler angle of the train at time t; Based on the quaternion, the posture of the train is updated during operation, and the updated model is: Among them, Q t is the quaternion describing the posture of the train at time t, Represents the quaternion Q t The derivative of They represent the pitch angular velocity, roll angular velocity and yaw angular velocity of the train at time t respectively; The model for IMU autonomous navigation to calculate train posture, speed and travel distance is: Among them, s t represents the distance traveled by the train at time t, is the speed of the train at time t, I represents the operator that converts the attitude quaternion into the acceleration of gravity in the direction of train movement; 4×4 represents the 4th-order identity matrix.

2. A train positioning method based on the fusion of on-board IMU and track geometric feature data according to claim 1, characterized in that: In step S3, the map matching process includes extracting key points for measuring curvature. Specifically, the curvature value is calculated using the train speed inferred from the IMU autonomous navigation and the measured angular velocity information. The calculation method is: Among them, κ t Represents the measured curvature value at time t.

3. The train positioning method based on the fusion of on-board IMU and track geometric feature data according to claim 2 is characterized in that: In step S4, the curvature of the track plane at the circular curve and the angular velocity of the train when passing through the circular curve are used to calculate the speed of the train. This speed is used as the true value information to calibrate the track slope information and the longitudinal acceleration output by the IMU to calculate the train's travel speed; The specific process includes: ① Train speed based on track design slope: The train speed is calculated by fusing the designed track slope information with the longitudinal acceleration output by the IMU. The calculation formula is: in, are the train running acceleration and speed compensated for the track design slope, respectively; is a discrete spatial sampling dataset of orbits; is the spatial value of the rail slope; ②Calculate the speed of the train on a circular curve: The running speed of the train on the circular curve of the track plane is calculated by fusing the design curvature information of the track and the train angular velocity measured by the IMU. Its expression is: in, is the running speed of the train on the circular curve, which serves as the true value information of the train speed; is the spatial value of the rail curvature; Represents a set of circular curve space sampling points; ③Train speed correction: First, within an operating range, the train speed for design slope compensation is expressed as: in, is the designed gradient compensation speed of the train at t1; the running time series of the train in this interval is expressed as TS = [t1, t2, ..., t j ,…,t N ] T , t j =jΔt; j represents the jth sampling time, and N represents the total number; Secondly, the running speed of the train on the curve in the interval is taken as the true value and expressed as: Where J represents the total number of speed samples of the train on the circular curve in the section, and the time series of the train running on the curve is recorded as T cir =[t'1,t'2,…,t' k ,…,t' J ] T ,and Finally, the designed slope compensation speed is calibrated using the true value, and the deviation between the true value speed and the designed slope compensation speed at the curve position is recorded as Its expression is: in accordance with Calculate the difference between all slope compensation and true value in this interval to get the speed difference that needs to be calibrated This information is obtained by the linear interpolation algorithm, and the calculation formula is: Where interp(·) is a linear interpolation operator; Therefore, the corrected train speed is: in, is the corrected train speed.

4. The train positioning method based on the fusion of on-board IMU and track geometric feature data according to claim 3 is characterized in that: This method uses the average positioning error to evaluate the positioning performance of the train. The formula is: in, is the average positioning error, I represents the number of key points in the interval, m kpi Indicates the absolute mileage information of the i-th key point in the interval, l i Indicates that the train is in [m kpi ,m kp(i+1) ] within the running distance.

5. A train positioning system based on the fusion of onboard IMU and track geometry data, characterized in that: A train positioning method based on the fusion of on-board IMU and track geometric feature data as described in any one of claims 1 to 4 is applied to perform train positioning, the system comprising: The data preprocessing module is used to preprocess the measurement data collected by a single on-board IMU, divide the entire route into sections, and obtain the on-board IMU measurement data for each section; The IMU navigation calculation module is used to perform on-board IMU autonomous navigation calculation and calculate the initial running speed and mileage of the train in the section through the on-board IMU autonomous navigation calculation model; The map matching module is used to perform map matching based on the curvature information of the track geometry and the information calculated by the on-board IMU autonomous navigation; The data fusion module is used to fuse the on-board IMU measurement data with the slope and curvature information of the track geometry based on map matching; calibrate the train running speed and update the train position.

6. An electronic device, characterized in that: It includes a processor and a memory, the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement a train positioning method based on the fusion of on-board IMU and track geometric feature data as described in any one of claims 1 to 4.

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