Train positioning method based on vehicle-mounted IMU (Inertial Measurement Unit) and track geometric feature data fusion

Abstract

The invention discloses a train positioning method based on vehicle-mounted IMU and track geometric feature data fusion, and belongs to the technical field of train positioning. The method comprises the following steps: preprocessing measurement data acquired by a single vehicle-mounted IMU, and dividing a line according to intervals to obtain vehicle-mounted IMU measurement data of each interval; vehicle-mounted IMU autonomous navigation calculation is carried out, and the initial running speed and the running mileage of the train in the interval are calculated through a vehicle-mounted IMU autonomous navigation calculation model; according to curvature information of track geometry, map matching is carried out based on autonomous navigation calculation information of a vehicle-mounted IMU (Inertial Measurement Unit); fusing the measurement information of the vehicle-mounted IMU with the geometric gradient and curvature information of the track; and calibrating the running speed of the train, and updating the position of the train. According to the method, output information of a single vehicle-mounted IMU is fused with geometric gradient and curvature information of a track, so that the running speed of the train is calibrated, and the train is further accurately positioned.

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 refers to determining the absolute mileage position of the train on the track, which refers to the distance from the starting point of the rail to the current position of the train along the track. Therefore, the urban rail transit positioning problem can be regarded as a one-dimensional positioning problem along the track space.

[0003] On-board IMU is usually used to assist the train positioning system. The IMU sensor is generally composed of a three-axis accelerometer and a three-axis gyroscope. The sensor is affected by its own random walk, resulting in cumulative errors in the train attitude, speed and travel distance it estimates. At present, the method of using on-board IMU for train positioning is usually based on the map matching technology of track curvature. The track curvature information is calculated according to the angular velocity output by the IMU and the speed information measured by the speed sensor, and it is matched with the track design curvature map, usually with the starting point and end point of the curve as matching information. At present, the map matching technology has two disadvantages: 1) The speed information depends on other speed sensors, such as the speed information of the train control system, which is difficult to obtain for non-invasive positioning systems; 2) It fails to reduce the cumulative error of the train running between two adjacent matching positions. The error gradually increases with the increase of the train running distance, thereby affecting the accuracy of the train position calculation.

[0004] In addition, without the help of map matching information, only one sensor, IMU, can be used to calculate the distance traveled by the train in the station section, and the section length is used as the true value for evaluation. The disadvantage of this method is that the rail mileage information where the IMU sensor is located is usually unknown. During the operation of the train, it can only be used to estimate the relative distance of the train operation. The algorithm cannot be used to estimate the track mileage position of the train. Therefore, the underground precise positioning technology of urban rail transit trains based on a single on-board IMU has not yet been realized. 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 fused 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 vehicle-mounted IMU, divide the entire route into sections, and obtain the vehicle-mounted IMU measurement data of each section;

[0009] S2, 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;

[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 on-board IMU measurement data is integrated with the slope and curvature information of the track geometry; the train running 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 of the train in the next section to calculate the train's posture and train mileage 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 that describe the posture of the train when it stops at the station; They are the average acceleration values ​​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 of the train in the running direction is:

[0018]

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

[0020] Based on quaternions, 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 running 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 of the measured curvature, specifically: using the train running speed inferred by the IMU autonomous navigation and the measured angular velocity information to calculate the curvature value, the calculation method is:

[0027]

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

[0029] The key point of the track curve is the spatial sequence information with mileage as coordinates, 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 on the track plane and the angular velocity of the train when passing through the circular curve are used to calculate the speed, and the speed is used as the true value information to calibrate the slope information of the track and the longitudinal acceleration output by the IMU to calculate the travel speed of the train; the specific process includes:

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

[0032] The train speed is calculated by integrating the design slope information of the track 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 integrating the design curvature information of the track and the train angular velocity measured by the IMU. The expression is:

[0037]

[0038] in, is the running speed of the train on the circular curve, which is 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, For trains at t 1 The design slope compensation speed; the train running time series in this section is expressed as TS = [t 1 ,t 2 ,…,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, 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 difference 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 represents 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, and applies the above-mentioned train positioning method based on the fusion of on-board IMU and track geometric feature data to perform train positioning, and the system includes:

[0058] The data preprocessing module is used to preprocess the measurement data collected by a single vehicle-mounted IMU, divide the entire route into sections, and obtain the vehicle-mounted IMU measurement data of 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 inferred by the onboard 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 geometry 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 realize the calibration of 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 partly become apparent from the description, or 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying 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 2A schematic diagram of the working principle 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.

[0071] Figure 3 A schematic diagram of the longitudinal acceleration of a train in 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 a map matching process provided by an embodiment of the present invention.

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

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

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

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

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

[0079] Fig.11 A schematic diagram of the error distribution of map matching and data fusion estimation within interval 16 provided in an embodiment of the present invention.

[0080] Fig.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 the description of the present invention, it should be noted that: in some processes described in the specification and drawings of this application, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be performed in the order in which they appear in this document or may be performed in parallel. In addition, various serial numbers are only used for descriptive purposes and cannot be understood 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 claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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, and the method mainly includes the following steps:

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

[0086] S2, 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;

[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 on-board 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 mode 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 on-board IMU and track geometry features:

[0091] 1. Data preprocessing:

[0092] The measurement data collected by the on-board IMU is the information of the entire line 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 sections and obtain the on-board IMU measurement data of each section, such as Figure 3 shown.

[0093] 2. IMU autonomous navigation calculation:

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

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

[0096]

[0097] Among them, φ int ,θ int , They are the Euler angles of nodding, rolling and shaking the head that describe the posture of the train when it stops at the station; They 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 running direction, represents the longitudinal acceleration of the train at time t, φ t is the nodding Euler angle of the train at time t.

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

[0102]

[0103] Among them, Q t is the quaternion representing 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.

[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 the embodiment of the present invention, the core idea of ​​the map matching method is to extract the key points of the curve and match them with the designed track key points to update the position of the train. 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, m 1 is the starting position of the spatial sampling sequence, m 1 +MΔm is the last sampling point of the spatial sampling sequence, that is, the termination position.

[0116] The curvature of the rail is the inverse of the radius of the rail plane curve. This paper adopts 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 IMU autonomous navigation and the measured angular velocity information of the train are used to calculate the measured curvature value, and the calculation method is as follows:

[0121]

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

[0123] The key point of the track curve is the spatial sequence information with mileage as coordinates, 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 key point position information of the design curvature is used to correct the position information of the key point 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. The 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 the embodiment of the present invention, on the basis of map matching, the measurement information of the IMU is further integrated with the slope and curvature information of the track map. In the process of map matching, it can be seen that the calculated curvature information has a certain deviation from the designed curvature information in terms of value, indicating that the speed error estimated by the IMU is large. Therefore, the curvature of the circular curve of the track plane and the angular velocity of the shaking head 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 travel speed of the train calculated by the slope information of the track and the longitudinal acceleration output by the IMU. The train speed information estimated by this process is calculated as follows.

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

[0129] According to the navigation calculation of IMU, the longitudinal acceleration of the train is related to the longitudinal acceleration measured by IMU and the pitch angle of the train. The pitch angle of the train is affected by the slope of the track. Therefore, the design slope information of the track is combined with the longitudinal acceleration output by IMU to estimate the train speed. 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 running speed of the train on the circular curve can be calculated by fusing the design curvature information of the track and the train angular velocity measured by the IMU. This speed is the most likely estimated value close to the actual running speed of the train. Its expression is:

[0134]

[0135] in, is the running speed of the train on the circular curve, which is the true value information of the train speed; Represents a set of circular curve space 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, For trains at t 1 The design slope compensation speed. The train operation time series in this section can be expressed as TS = [t 1 ,t 2 ,…,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, which 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. Indicates the distance information of the first key point in the section relative to the train departure position. Through map matching, the relative distance position is converted into its corresponding absolute track mileage information.

[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 for evaluating the train positioning accuracy, where the parking position of the center of the train is assumed to be the central 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 estimate is an estimate of the distance traveled by the train in an interval, but it cannot accurately represent the train's positioning accuracy. 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 demarcation point is used to evaluate the train positioning accuracy, expressed as:

[0155]

[0156] Among them, I represents the number of key points in the interval, m kpi represents 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 running distance within .

[0157] The method for evaluating the train positioning performance adopted by the present invention is particularly suitable for situations where the true value of the train track 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 result. 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 a true value trajectory: It is usually very difficult and time-consuming to establish the true value information of the train track 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 detection, accurately locating the location of rail defects is crucial for maintenance and detection 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 verifies the applicability of the method through field experiments and evaluates the train positioning accuracy of the method. The results show that the average positioning error of the method within a certain interval is less than 1%. The MEMSIMU data installed on different vehicle models are tested, and the results show that the system has good reliability. The experiment of the present invention is introduced below:

[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 operation 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 groups 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 vehicle-mounted IMU and rail geometry data fusion algorithm, the cumulative interval length error of the entire line is less than 1.5%. Fig. 9 and Fig.10 As shown in the figure, the train speed-time curve results and the corresponding estimated curvatures for three typical sections are plotted. GT speed is the speed of the train on the arc, which represents the true value, and DF represents the data fusion algorithm. The results show that the measured curvature estimated based on the corrected speed is consistent with the track design curvature in shape, amplitude and mileage, indicating that the algorithm is suitable for engineering applications.

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

[0166] The data fusion algorithm proposed in the present 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 shorter track interval between two adjacent key points in the interval. Fig.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] It can be seen from Table 1 that without further correction by DF, the average positioning error of map matching may exceed 100 meters in some intervals, which means that in this case, the railway inspector must walk at least 100 meters to locate the track irregularity. This is an inefficient and laborious method for the inspection task. However, the data fusion step further reduces the average positioning error to about 20 meters, and the error percentage is less than 1%, which is feasible in engineering applications and meets the actual needs of track inspection.

[0171] From the description of the above embodiments, those skilled in the art can know that the present invention proposes a train positioning method based on the fusion of on-board IMU and track geometry feature data, which uses the output information of a single on-board IMU to fuse with the slope and curvature information of the track geometry to achieve calibration of the train running speed, further achieve accurate positioning of the train, and improve the accuracy of train positioning. In addition, the present invention also proposes to estimate the average positioning error to evaluate the positioning performance of the train, which provides an effective solution for evaluating the positioning performance of the train in the underground environment.

[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 vehicle-mounted IMU, divide the entire route into sections, and obtain the vehicle-mounted IMU measurement data of each section;

[0174] The IMU autonomous navigation calculation module is used to perform vehicle-mounted IMU autonomous navigation calculation, and calculate the initial running speed and mileage of the train in 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 inferred 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, parts not mentioned in this embodiment can be referred to the corresponding contents in the aforementioned method embodiment, which will not be repeated here.

[0178] Also, see Fig.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, and may also include a computer program stored in the memory 11 and executable on the processor 10, and the processor executes the computer program to implement a train positioning method based on the fusion of on-board IMU and track geometry feature data in the above method embodiment.

[0179] The processor 10 may be composed of an integrated circuit in some embodiments, for example, 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 combinations of various control chips. The processor 10 is the control core of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory 11, and calling data stored in the memory 11.

[0180] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, electronic devices or computer program products, etc. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[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 a number of different components and by means of a suitably programmed computer. The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other.

[0182] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but rather 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 vehicle-mounted IMU, divide the entire route into sections, and obtain the vehicle-mounted IMU measurement data of each section; S2, 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; 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 on-board IMU measurement data is integrated with the slope and curvature information of the track geometry; the train running speed is calibrated and the train position is updated.

2. A train positioning method based on vehicle-mounted IMU and track geometric feature data fusion according to claim 1, characterized in that: In step S2, the IMU autonomous navigation calculation process includes two states: train stop and train running, where: When the train stops at a station, the train's posture is updated and used as the initial posture of the train in the next section to calculate the train's posture and train mileage during operation. The initial posture calculation model is: Among them, φ int ,θ int , They are the nodding, rolling and shaking Euler angles that describe the posture of the train when it stops at the station; They are the average acceleration values ​​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 of the train in the running direction is: in, is the acceleration of the train in the running direction, represents the longitudinal acceleration of the train at time t, φ t is the nodding Euler angle of the train at time t; Based on quaternions, 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 running 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.

3. A train positioning method based on vehicle-mounted IMU and track geometric feature data fusion according to claim 2, characterized in that: In step S3, the map matching process includes extracting key points of the measured curvature, specifically: using the train running speed calculated by the IMU autonomous navigation and the measured angular velocity information to calculate the curvature value, the calculation method is: Among them, κ t Represents the measured curvature value at time t.

4. A train positioning method based on vehicle-mounted IMU and track geometric feature data fusion according to claim 3, characterized in that: In step S4, the curvature of the circular curve on the track plane and the angular velocity of the train when passing through the circular curve are used to calculate the speed of the train. The speed is used as the true value information to calibrate the slope information of the track and the longitudinal acceleration output by the IMU to calculate the running speed of the train. The specific process includes: ①Train speed based on track design slope: The train speed is calculated by integrating the design slope information of the track 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 integrating the design curvature information of the track and the train angular velocity measured by the IMU. The expression is: in, is the running speed of the train on the circular curve, which is 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 slope 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, 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′ j ,…,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: 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 difference 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.

5. A train positioning method based on vehicle-mounted IMU and track geometric feature data fusion according to claim 4, 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 represents 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.

6. A train positioning system based on the fusion of on-board IMU and track geometric feature 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 5 is applied to perform train positioning, the system comprising: The data preprocessing module is used to preprocess the measurement data collected by a single vehicle-mounted IMU, divide the entire route into sections, and obtain the vehicle-mounted IMU measurement data of 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 inferred by the onboard 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.

7. 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 geometry feature data as described in any one of claims 1-5.

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