Train positioning method based on Kalman filtering algorithm

Abstract

The invention discloses a train positioning method based on a Kalman filtering algorithm, and the method comprises the steps: determining the first position information of a vehicle-mounted UWB base station in a current period according to a first projection distance obtained by mapping the first distances between the ground UWB base stations and the vehicle-mounted UWB base station to a track plane, wherein the first distances are measured by at least three ground UWB base stations; according to the second position information of the vehicle-mounted UWB base station determined in the previous period, predicting third position information of the vehicle-mounted UWB base station in the current period based on a Kalman filtering algorithm; and fusing the first position information and the third position information based on a Kalman filtering algorithm to obtain fourth position information of the vehicle-mounted UWB base station in the current period. And finally, according to the relative position relationship between the vehicle-mounted UWB base station and the train, determining the target position information of the train in the current period. The train positioning cost is reduced, the problem of inaccurate train positioning caused by wireless communication delay or packet loss is avoided, and the train positioning accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of rail transit technology, and in particular to a train positioning method based on the Kalman filter algorithm. Background Technology

[0002] With the development of urban rail transit signaling technology, the requirements for operational safety and efficiency in urban rail transit are becoming increasingly stringent. For example, in the subway system, if the positioning of subway trains is accurate, the number of subway trains operating simultaneously on the subway line can be increased while ensuring safe operation, thereby reducing passenger waiting time and improving operational efficiency.

[0003] In existing rail transit signaling systems, train positioning relies on onboard equipment for calculations. This onboard equipment periodically reports train position information to ground equipment via vehicle-to-ground wireless communication. The error in the onboard equipment's train positioning calculations is proportional to the distance the train has traveled after passing a transponder. This necessitates the placement of a transponder at regular intervals to correct the train's position, resulting in high costs for train positioning. Furthermore, delays or packet loss in vehicle-to-ground wireless communication cause uncertainty in train position, leading to significant discrepancies between the train positioning obtained by ground equipment and the actual train position. Therefore, the accuracy of existing train positioning systems is relatively poor. Summary of the Invention

[0004] This application provides a train positioning method based on the Kalman filter algorithm to solve the problem of poor accuracy in existing train positioning technologies.

[0005] In a first aspect, this application provides a train positioning method based on the Kalman filter algorithm, the method comprising:

[0006] For at least three terrestrial UWB base stations, a first distance between each terrestrial UWB base station and the vehicle-mounted UWB base station is obtained according to a preset period. Based on each first distance obtained in the current period and the pre-saved height difference between each terrestrial UWB base station and the vehicle-mounted UWB base station in the vertical track plane direction, each first distance is determined as a first projection distance on the track plane. Based on the position information of each terrestrial UWB base station and the first projection distance, the first position information of the vehicle-mounted UWB base station in the current period is determined.

[0007] Based on the second location information of the vehicle-mounted UWB base station determined in the previous cycle, the third location information of the vehicle-mounted UWB base station in the current cycle is predicted based on the prediction state equation in the Kalman filter algorithm.

[0008] Based on the first location information and the third location information of the vehicle-mounted UWB base station, and using the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station in the current period is determined.

[0009] Based on the fourth location information of the vehicle-mounted UWB base station and the relative positional relationship between the vehicle-mounted UWB base station and the train, the target location information of the train in the current period is determined.

[0010] The above technical solution has the following advantages or beneficial effects:

[0011] In related technologies, the train positioning calculation error of on-board equipment is proportional to the train's travel distance after passing a transponder. This necessitates the deployment of a transponder at regular intervals to correct the train's position, resulting in high costs for train positioning. Furthermore, the uncertainty in train position caused by delays or packet loss in vehicle-to-ground wireless communication leads to significant deviations between the train positioning obtained by ground equipment and the actual train position, resulting in poor positioning accuracy. To address the high costs associated with deploying multiple transponders and the poor positioning accuracy caused by vehicle-to-ground communication, this application proposes a train positioning method based on a Kalman filter algorithm. First, for at least three ground UWB base stations, the first distance between the ground UWB base station and the on-board UWB base station, measured at a preset period, is obtained. Then, the first distances measured by each ground UWB base station in the current period are mapped onto the track plane to obtain the first projected distance. Finally, based on the position information of each ground UWB base station and the first projected distances, the first position information of the on-board UWB base station in the current period is determined. Then, based on the second location information of the vehicle-mounted UWB base station determined in the previous cycle, the third location information of the vehicle-mounted UWB base station in the current cycle is predicted based on the prediction state equation in the Kalman filter algorithm. The first and third location information are then fused based on the state equation in the Kalman filter algorithm to obtain the fourth location information of the vehicle-mounted UWB base station in the current cycle. Finally, based on the relative positional relationship between the vehicle-mounted UWB base station and the train, the target location information of the train in the current cycle is determined. This application eliminates the need for multiple transponders, reducing the cost of train positioning. Furthermore, this application achieves train positioning based on Kalman filtering, avoiding the inaccurate train positioning caused by wireless communication delays or packet loss in related technologies that determine train position via vehicle-to-ground wireless communication, thus improving the accuracy of train positioning.

[0012] Secondly, this application provides a train positioning device based on the Kalman filter algorithm, the device comprising:

[0013] The first determining module is configured to: acquire, for at least three terrestrial UWB base stations, a first distance between each terrestrial UWB base station and a vehicle-mounted UWB base station measured at a preset period; determine, based on each first distance acquired in the current period and a pre-saved height difference between each terrestrial UWB base station and the vehicle-mounted UWB base station in the vertical track plane direction, a first projection distance of each first distance on the track plane; and determine, based on the position information of each terrestrial UWB base station and the first projection distance, the first position information of the vehicle-mounted UWB base station in the current period.

[0014] The prediction module is used to predict the third location information of the vehicle-mounted UWB base station in the current period based on the second location information of the vehicle-mounted UWB base station determined in the previous period and the prediction state equation in the Kalman filter algorithm.

[0015] The second determining module is used to determine the fourth location information of the vehicle-mounted UWB base station in the current period based on the first location information and the third location information of the vehicle-mounted UWB base station and the state equation in the Kalman filter algorithm.

[0016] The third determining module is used to determine the target position information of the train in the current period based on the fourth position information of the vehicle-mounted UWB base station and the relative positional relationship between the vehicle-mounted UWB base station and the train.

[0017] Thirdly, this application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0018] Memory, used to store computer programs;

[0019] A processor, used to execute a program stored in memory, implements the method described.

[0020] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described herein.

[0021] Fifthly, this application provides a computer program product comprising an executable program that is executed by a processor to implement the method described. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic diagram of the first train positioning process based on the Kalman filter algorithm provided in this application;

[0024] Figure 2 A schematic diagram illustrating the process of predicting the third location information of the vehicle-mounted UWB base station in the current period, as provided in this application;

[0025] Figure 3 A schematic diagram illustrating the process of determining the fourth location information of the vehicle-mounted UWB base station in the current period, as provided in this application;

[0026] Figure 4 A schematic diagram of the second train positioning process based on the Kalman filter algorithm provided in this application;

[0027] Figure 5 A flowchart of train positioning based on the Kalman filter algorithm provided for this application;

[0028] Figure 6 This is a schematic diagram of the train positioning scenario provided in this application;

[0029] Figure 7 A schematic diagram of least squares positioning provided for this application;

[0030] Figure 8 The flowchart of the Kalman filter algorithm provided in this application;

[0031] Figure 9 A schematic diagram illustrating the position estimation of the vehicle-mounted UWB base station on the track provided in this application;

[0032] Figure 10 This is a schematic diagram of the train position envelope provided in this application;

[0033] Figure 11 A schematic diagram of the train positioning device based on the Kalman filter algorithm provided in this application;

[0034] Figure 12 A schematic diagram of the electronic device structure provided in this application. Detailed Implementation

[0035] To make the objectives and implementation methods of this application clearer, the exemplary implementation methods of this application will be clearly and completely described below with reference to the accompanying drawings of the exemplary embodiments of this application. Obviously, the exemplary embodiments described are only some embodiments of this application, and not all embodiments.

[0036] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0037] The terms "first," "second," "third," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar or related objects or entities, and do not necessarily imply a specific order or sequence, unless otherwise specified. It should be understood that such terms are interchangeable where appropriate.

[0038] The terms “comprising” and “having”, and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device that includes a range of components is not necessarily limited to all of the components that are clearly listed, but may include other components that are not clearly listed or that are inherent to such product or device.

[0039] The term "module" refers to any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0041] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the above exemplary discussion is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the principles and practical applications, thereby enabling those skilled in the art to better utilize the described embodiments and various different variations of embodiments suitable for specific use considerations.

[0042] Figure 1 The first schematic diagram of a train positioning process based on the Kalman filter algorithm provided in this application includes the following steps:

[0043] S101: For at least three terrestrial UWB base stations, obtain the first distance between each terrestrial UWB base station and the vehicle-mounted UWB base station measured according to a preset period; determine the first projection distance of each first distance on the track plane based on each first distance obtained in the current period and the pre-saved height difference between each terrestrial UWB base station and the vehicle-mounted UWB base station in the vertical track plane direction; determine the first position information of the vehicle-mounted UWB base station in the current period based on the position information of each terrestrial UWB base station and the first projection distance.

[0044] S102: Based on the second location information of the vehicle-mounted UWB base station determined in the previous cycle, predict the third location information of the vehicle-mounted UWB base station in the current cycle based on the prediction state equation in the Kalman filter algorithm.

[0045] S103: Based on the first location information and the third location information of the vehicle-mounted UWB base station, and using the state equation in the Kalman filter algorithm, determine the fourth location information of the vehicle-mounted UWB base station for the current period;

[0046] S104: Based on the fourth location information of the vehicle-mounted UWB base station and the relative positional relationship between the vehicle-mounted UWB base station and the train, determine the target location information of the train in the current period.

[0047] The train positioning method based on the Kalman filter algorithm provided in this application can be applied to electronic devices, such as PCs, computers, smartphones, and other terminal devices, as well as servers, ground computer equipment, etc.

[0048] The train positioning method based on the Kalman filter algorithm provided in this application involves ranging information from a UWB base station. A UWB base station is a base station designed and developed based on Ultra-Wideband (UWB) technology.

[0049] When deploying ground-based UWB base stations, it is essential to ensure that at any given time, the train's onboard UWB base station has a visual line of sight to at least three ground-based UWB base stations. Generally, onboard UWB base stations are deployed at both ends of the train. A visual line of sight to a ground-based UWB base station refers to a ground-based UWB base station within the communication range of the onboard UWB base station that is unobstructed from it. For at least three ground-based UWB base stations, the electronic equipment acquires the initial distance between the ground-based UWB base station and the onboard UWB base station, measured at a preset period.

[0050] Based on the first distances acquired in the current cycle and the pre-saved height differences between each ground-based UWB base station and the vehicle-mounted UWB base station in the vertical orbital plane direction, the first projection distances of each first distance on the orbital plane are determined. Let the measured first distance be d. im d im The projection onto the orbital plane is d. i The height difference between the ground-based UWB base station and the vehicle-mounted UWB base station to be located is d. iz Then, d i 2 =d im 2 -d iz 2 .

[0051] Based on the location information of each ground UWB base station and the first projection distance, the first location information of the vehicle-mounted UWB base station in the current period is determined using the least squares method.

[0052] Optionally, the process of determining the first location information of the vehicle-mounted UWB base station includes:

[0053] Let the first location information of the vehicle-mounted UWB base station be... Location information of ground UWB base stations is x is the coordinate axis along the track direction in the track plane, and y is the coordinate axis perpendicular to the track direction in the track plane; the first projected distance corresponding to the first distance measured by the ground UWB base station is d. i , where d i 2 =(x j -x i ) 2 +(y j -y i ) 2 Let k i 2 =(x i ) 2 +(y i ) 2 The matrix is ​​obtained based on the location information of the ground UWB base stations. Matrix construction based on least squares method According to the least squares method X j =(A T A) -1 A T B, determine the first location information X of the vehicle-mounted UWB base station. j Where n is the number of vehicle-mounted UWB base stations.

[0054] It should be noted that if the at least three terrestrial UWB base stations are collinear, the aforementioned matrix A... T A is irreversible. In this case, the process of determining the first location information of the vehicle-mounted UWB base station includes:

[0055] make According to the least squares method A x T A x x j＝ A x T B, calculate x j ; to x j Substitute d i 2 =(x j -x i ) 2 +(y ji -y i ) 2 y is calculated ji ;

[0056] Let y ji With y j The distance between them is d ji d ji 2 =(y ji -y j ) 2 ;make According to the least squares method A j T A j y j =A j T B j y is calculated j ;

[0057] Where, x jn y jn This refers to the location information of the nth ground UWB base station at time j.

[0058] It should be noted that if the period for measuring the first distance between the ground UWB base station and the vehicle-mounted UWB base station is short, while the period for the electronic equipment to locate the train is long, multiple first distances measured by each ground UWB base station are obtained within the current positioning period. In this case, for at least three ground UWB base stations, for each first distance measured by the ground UWB base station at multiple moments within the current positioning period, a preset distance threshold is used as a filtering condition. Among the various first distances, those greater than the preset distance threshold are filtered out. Then, interpolation is performed based on the first distances measured at adjacent moments corresponding to the filtered first distances to obtain the interpolated distance. Optionally, the average of the first distances at consecutive moments is used as the interpolated distance. If the filtered first distance is the distance at the endpoint moment, this first distance is considered noise. If the noise is at the right boundary point D... i =2D i-1 -D i-2 If the noise point is at the left boundary point D i =2D i+1 -D i+2 D i D represents the interpolated distance corresponding to the noise point. i-1 D is the first distance measured between adjacent time points corresponding to the measurement time of the right boundary noise point. i-2 D i-1 The first distance measured at another adjacent time point to the corresponding measurement time. D i+1 D is the first distance measured between adjacent time points corresponding to the measurement time of the left boundary noise point. i+2 D i+1 The first distance is measured at another adjacent time point. The noise distance is replaced with the interpolated distance after interpolation processing to obtain updated first distances. Then, the first projection distances of the updated first distances on the track plane are determined. It should be noted that if the first distances measured at two consecutive time points are both greater than a preset distance threshold, then all first distances measured in this group are considered invalid. This application ensures the accuracy of each first distance by filtering out first distances greater than the preset distance threshold and then interpolating the distances measured at adjacent time points to obtain updated distances, thereby ensuring the accuracy of subsequent train positioning.

[0059] Furthermore, in this application, the median distance of each updated first distance is determined. It should be noted that, for at least three ground UWB base stations, for each first distance measured by the ground UWB base station at multiple times within the current positioning period, if one of the first distances is replaced, then all first distances including the replaced first distance are used as updated first distances. Then, the updated first distances are grouped together. For each updated first distance, a reference distance threshold is determined, and then, using the reference distance threshold as a filtering condition, first distances greater than the corresponding reference distance threshold are filtered out. Then, interpolation is performed based on the first distances measured at adjacent times corresponding to the measured time of the filtered first distance to obtain the interpolated distance. This application performs a second update on the updated first distances, further improving the accuracy of each first distance, thereby ensuring the accuracy of subsequent train positioning.

[0060] The process of determining the reference distance threshold corresponding to the first distance includes:

[0061] According to formula D 参考 =NvT uwb +3σ s +E a Determine the reference distance threshold corresponding to the first distance;

[0062] Among them, D 参考 To reference the distance threshold, multiple time intervals are determined sequentially, where N is the time interval difference between the time interval corresponding to the first distance and the time interval corresponding to the median distance, v is the train's speed information at the time interval corresponding to the first distance, and T... uwb For the preset period, NvT uwb For the second travel distance, σ s It is the static statistical ranging standard deviation, E, determined in advance based on sample distances measured by ground UWB base stations when the train is stationary. a This is the preset first error value.

[0063] In this application, based on the time sequence number corresponding to each of multiple times, the time sequence number difference between the time corresponding to the first distance and the time corresponding to the median distance, the train's running speed information at the time corresponding to the first distance, a preset period, the static statistical ranging standard deviation determined in advance based on the sample distance measured by the ground UWB base station when the train is stationary, and a preset first error value, are substituted into formula D. 参考 =NvT uwb +3σ s +E a A reference distance threshold is determined for this first distance. This improves the accuracy of determining the reference distance threshold.

[0064] In this application, based on the second location information of the vehicle-mounted UWB base station determined in the previous cycle, the third location information of the vehicle-mounted UWB base station in the current cycle is predicted based on the prediction state equation in the Kalman filter algorithm.

[0065] Specifically, Figure 2 The process diagram for predicting the third location information of the vehicle-mounted UWB base station in the current period provided in this application includes the following steps:

[0066] The step of predicting the third location information of the vehicle-mounted UWB base station in the current period based on the second location information of the vehicle-mounted UWB base station determined in the previous period and the prediction state equation in the Kalman filter algorithm includes:

[0067] S201: Based on the speed of the train determined from the previous period and the preset period, predict the acceleration of the train in the current period; determine the control vector matrix based on the acceleration and the preset period;

[0068] S202: Based on the pre-saved state vector state transition matrix, the second location information of the vehicle-mounted UWB base station determined in the previous period, and the control vector matrix, predict the third location information of the vehicle-mounted UWB base station in the current period based on the prediction state equation in the Kalman filter algorithm.

[0069] Specifically, based on the train speed determined from the previous cycles and the preset cycle, according to the formula... Predict the acceleration of the train in the current cycle; T is the preset cycle, a k v is the acceleration of the train in the current period k. (k-1) The speed of the train determined by the period k-1, v (k-2) The speed of the train determined by the period k-2;

[0070] The control vector matrix is ​​determined based on the acceleration and the preset period. a kx Let a be the acceleration component along the track direction in the track plane. ky This represents the acceleration component in the orbital plane along the direction perpendicular to the orbit.

[0071] Based on the state transition matrix of the pre-saved state vector The second location information X of the vehicle-mounted UWB base station determined in the previous cycle (k-1) and the control vector matrix B k u kBased on the prediction state equation in the Kalman filter algorithm, the third location information of the vehicle-mounted UWB base station in the current period is predicted as follows:

[0072] Based on the first and third location information of the vehicle-mounted UWB base station, and using the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station for the current period is determined.

[0073] Figure 3 The process diagram for determining the fourth location information of the vehicle-mounted UWB base station in the current period provided in this application includes the following steps:

[0074] Based on the first and third location information of the vehicle-mounted UWB base station, and using the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station for the current period is determined as follows:

[0075] S301: Determine the acceleration variance based on the train's predicted acceleration in the current cycle, the train's predicted acceleration in the previous cycle, and the preset cycle; determine the process noise covariance matrix based on the acceleration variance and the preset cycle;

[0076] S302: Determine the prediction covariance matrix for the current period based on the covariance matrix determined in the previous period, the state transition matrix of the state vector, and the process noise covariance matrix;

[0077] S303: Based on the predicted covariance matrix of the current period, the pre-saved observation matrix, and the pre-determined noise covariance matrix, determine the Kalman gain according to the Kalman gain calculation equation in the Kalman filtering algorithm;

[0078] S304: Based on the first location information, the third location information, the Kalman gain, and the observation matrix of the vehicle-mounted UWB base station, and using the state equation in the Kalman filtering algorithm, determine the fourth location information of the vehicle-mounted UWB base station for the current period.

[0079] Specifically, based on the predicted acceleration a of the train in the current cycle k The train's predicted acceleration a in the previous cycle k-1 And the preset period T, determine the acceleration variance as

[0080] According to the acceleration variance The process noise covariance matrix is ​​determined by the preset period T. Where, σ kx 2Let σ be the variance component of the acceleration along the track direction in the track plane. ky 2 The variance component of acceleration along the direction perpendicular to the track in the track plane;

[0081] Based on the covariance matrix P determined in the previous period (k-1) The state transition matrix F of the state vector k and the process noise covariance matrix Q k The prediction covariance matrix for the current period is determined as follows:

[0082] Based on the predicted covariance matrix of the current period Pre-saved observation matrix and a predetermined noise covariance matrix Based on the Kalman gain calculation equation in the Kalman filter algorithm, the Kalman gain is determined as follows: Where, σ x and σ y These are the pre-calibrated noise standard deviations;

[0083] According to the first location information of the vehicle-mounted UWB base station The third location information The Kalman gain K and the observation matrix H k Based on the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station in the current period is determined as follows: Where, p zx p is the first position component along the track direction in the track plane. zy It is the second position component along the track direction in the track plane.

[0084] Based on the fourth location information of the vehicle-mounted UWB base station and the relative positional relationship between the vehicle-mounted UWB base station and the train, the target location information of the train in the current period is determined.

[0085] Let Y be the fourth location information of the vehicle-mounted UWB base station. j The train's location information is P. j If the relative positional relationship vector between the vehicle-mounted UWB base station and the train is D, then P j =Y j +D.

[0086] In related technologies, the train positioning calculation error of on-board equipment is proportional to the train's travel distance after passing a transponder. This necessitates the deployment of a transponder at regular intervals to correct the train's position, resulting in high costs for train positioning. Furthermore, the uncertainty in train position caused by delays or packet loss in vehicle-to-ground wireless communication leads to significant deviations between the train positioning obtained by ground equipment and the actual train position, resulting in poor positioning accuracy. To address the high costs associated with deploying multiple transponders and the poor positioning accuracy caused by vehicle-to-ground communication, this application proposes a train positioning method based on a Kalman filter algorithm. First, for at least three ground UWB base stations, the first distance between the ground UWB base station and the on-board UWB base station, measured at a preset period, is obtained. Then, the first distances measured by each ground UWB base station in the current period are mapped onto the track plane to obtain the first projected distance. Finally, based on the position information of each ground UWB base station and the first projected distances, the first position information of the on-board UWB base station in the current period is determined. Then, based on the second location information of the vehicle-mounted UWB base station determined in the previous cycle, the third location information of the vehicle-mounted UWB base station in the current cycle is predicted based on the prediction state equation in the Kalman filter algorithm. The first and third location information are then fused based on the state equation in the Kalman filter algorithm to obtain the fourth location information of the vehicle-mounted UWB base station in the current cycle. Finally, based on the relative positional relationship between the vehicle-mounted UWB base station and the train, the target location information of the train in the current cycle is determined. This application eliminates the need for multiple transponders, reducing the cost of train positioning. Furthermore, this application achieves train positioning based on Kalman filtering, avoiding the inaccurate train positioning caused by wireless communication delays or packet loss in related technologies that determine train position via vehicle-to-ground wireless communication, thus improving the accuracy of train positioning.

[0087] In this application, the method further includes:

[0088] Based on the predicted covariance matrix of the current period The Kalman gain K and the observation matrix H k Based on the covariance equation in the Kalman filter algorithm, the covariance matrix of the current period is determined as follows: in, The initial value of the covariance matrix is

[0089] Specifically, based on the first location information, the third location information, the Kalman gain, and the observation matrix of the vehicle-mounted UWB base station, and using the state equation in the Kalman filtering algorithm, the fourth location information of the vehicle-mounted UWB base station for the current period is determined, including:

[0090] According to the first location information of the vehicle-mounted UWB base station The third location information The Kalman gain K and the observation matrix H k Based on the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station and the speed of the train in the current period are determined as follows: Among them, v kx v is the velocity component along the track direction in the track plane of the train determined by the current period k. ky The velocity component of the train in the track plane along the direction perpendicular to the track, determined for the current period k.

[0091] In this application, after determining the fourth location information of the vehicle-mounted UWB base station and before determining the location information of the train in the current period, the method further includes:

[0092] The fourth location information of the vehicle-mounted UWB base station is mapped onto the track to obtain the first mapped location information on the track closest to the vehicle-mounted UWB base station; the first mapped location information is used as the fourth location information of the vehicle-mounted UWB base station.

[0093] After determining the target location information of the train in the current period, the method further includes:

[0094] The target position information of the train is mapped onto the track to obtain a second mapped position information on the track closest to the train; this second mapped position information is then used as the target position information of the train. This improves the accuracy of determining the train's target position information.

[0095] Figure 4 The second train positioning process based on the Kalman filter algorithm provided in this application includes the following steps:

[0096] S401: For at least three terrestrial UWB base stations, obtain the first distance between each terrestrial UWB base station and the vehicle-mounted UWB base station measured according to a preset period; determine the first projection distance of each first distance on the track plane based on each first distance obtained in the current period and the pre-saved height difference between each terrestrial UWB base station and the vehicle-mounted UWB base station in the vertical track plane direction; determine the first position information of the vehicle-mounted UWB base station in the current period based on the position information of each terrestrial UWB base station and the first projection distance.

[0097] S402: Based on the second location information of the vehicle-mounted UWB base station determined in the previous cycle, predict the third location information of the vehicle-mounted UWB base station in the current cycle based on the prediction state equation in the Kalman filter algorithm;

[0098] S403: Based on the first location information and the third location information of the vehicle-mounted UWB base station, and using the state equation in the Kalman filter algorithm, determine the fourth location information of the vehicle-mounted UWB base station for the current period;

[0099] S404: Based on the fourth location information of the vehicle-mounted UWB base station and the relative positional relationship between the vehicle-mounted UWB base station and the train, determine the target location information of the train in the current period;

[0100] S405: Determine a first error value based on the position parameters in the covariance matrix of the current period; determine a second error value based on the speed of the train in the current period and the preset period; determine an envelope adjustment value based on the first error value, the second error value, and the preset third error value; determine the envelope position information of the train based on the target position information of the train in the current period and the envelope adjustment value.

[0101] Specifically, based on the covariance matrix P of the current period k The position parameter σ in px 2 and σ py 2 Determine the first error value The train's speed v according to the current cycle e And the preset period T, determine the second error value σv=v e T; where, Based on the first error value σ, the second error value σv, and the preset third error value σf, the envelope adjustment value is determined to be σe = 6σ + σv + σf.

[0102] In this application, the method further includes:

[0103] If the first location information of the vehicle-mounted UWB base station in the current period cannot be determined, according to the formula Determine the fourth location information of the vehicle-mounted UWB base station in the current period and the speed of the train in the current period; wherein, v mx v is the velocity component along the track direction in the track plane of the train, determined by the previous period m. my The velocity component of the train in the track plane perpendicular to the track direction, determined by the previous period m; a mx a is the acceleration component along the track direction in the track plane of the train, determined by the previous period m;my The acceleration component of the train in the track plane perpendicular to the track direction, as determined by the previous period m;

[0104] The second error value is determined to be in, a m Let m be the acceleration of the train during the preceding period.

[0105] In this application, the envelope adjustment value is determined according to the formula σe=6σ+σv+σf. Then, based on the train's target position information and the envelope adjustment value, the train's envelope position information is determined. This improves the accuracy of determining the train's envelope position information, thereby ensuring the safety of train operation.

[0106] Specifically, for the front position of the train, the envelope adjustment value is added forward to obtain the position information of the maximum safe front end, and the envelope adjustment value is subtracted backward to obtain the position information of the minimum safe front end; for the rear position of the train, the envelope adjustment value is added backward to obtain the position information of the maximum safe rear end, and the envelope adjustment value is subtracted forward to obtain the position information of the minimum safe rear end.

[0107] This application proposes a method for calculating the train safety envelope and train speed based on the Kalman filter algorithm. The core equipment of the rail transit signaling system can calculate the train safety envelope position and train speed more accurately. In this application, the ground equipment in the core rail transit signaling system can calculate the train position more accurately, thus reducing the train safety envelope. When the train degrades, the ground equipment can still calculate the train safety envelope, enabling the ground control system to make safer and more reliable emergency plans. Furthermore, the ground equipment in this scheme can independently calculate the train speed, without relying on train speed sensors and radar to calculate the train speed.

[0108] Figure 5 The train positioning flowchart based on the Kalman filter algorithm provided in this application includes:

[0109] Periodically collect multi-period ranging data from multiple ground-based UWB base stations and perform noise filtering and interpolation processing;

[0110] The location of a vehicle-mounted UWB base station at the time of ranging of a ground UWB base station is calculated using a multi-point positioning algorithm and the least squares method.

[0111] The Kalman filter algorithm was used to calculate the position of the vehicle-mounted UWB base station and the train speed at the time of ranging of each ground UWB base station.

[0112] Determine the train's safety envelope and speed at the current moment;

[0113] Kalman filter algorithm calculation is performed to determine the location information of the on-board UWB base station and thus the train speed;

[0114] The location of the vehicle-mounted UWB base station on the track is determined based on the location of the vehicle-mounted UWB base station calculated using the Kalman filter algorithm and the turnout direction.

[0115] The position of the train end is determined based on the location of the onboard WWB base station and the distance from the train end to the onboard WWB base station.

[0116] The uncertainty error of the vehicle end position is calculated based on the covariance matrix, train speed, UWB distance measurement cycle, and engineering test data.

[0117] The train safety envelope is calculated based on the vehicle end position and the uncertainty error of the vehicle end position.

[0118] 1. Based on the characteristics of the subway signaling system, a three-dimensional UWB positioning coordinate system is established. The z-axis is perpendicular to the track plane, with the track plane as the z-axis 0 point, the direction of the track equipment number as the x-axis direction, and the direction perpendicular to the track (or the straight track at turnouts) on the track plane as the y-axis. Since the relative height between the onboard UWB base station and the ground track is a constant, and the relative height between the ground UWB base station and the ground track is known, the three-dimensional rectangular coordinate system can be projected onto a two-dimensional rectangular coordinate system for simplified calculation when calculating UWB positioning. Figure 6 This is a schematic diagram of the train positioning scenario provided in this application. Due to the special environment of the subway signaling system, to reduce errors caused by non-line-of-sight propagation and multipath propagation, when deploying ground UWB base stations, it is necessary to ensure that at any given time, the UWB base station on the train end is visible to at least three ground UWB base stations. This is achieved by reducing the spacing between ground UWB base stations on sections with curves and slopes. When performing UWB positioning, noise filtering and interpolation processing of the UWB ranging information between ground UWB base stations and onboard UWB base stations are first required. Then, the onboard UWB base station at a certain time is located using a combination of a TOF and TDOA (Time of Flight (TOF) and Time Difference of Arrival (TDOA)) fusion algorithm and the least squares method. The calculated onboard UWB base station positioning at time k is obtained. Where k = 1, 2, 3…m. Figure 7 A schematic diagram of least squares positioning provided for this application.

[0119] Assume the location of the vehicle-mounted UWB base station to be located is p at a certain ranging time (time k). zk =X k ,Right now The location of the ground UWB base station is X i ,Right now The projection of the distance measurement between the ground-based UWB base station and the vehicle-mounted UWB base station to be located onto the track plane is d. i Where i = 1, 2, 3…n, k = 1, 2, 3…m, then d i2 =(x k -x i ) 2 +(y k -y i ) 2 ;

[0120] according to:

[0121] R i1 =d i -d1, R1=d1, R i =d i ;

[0122] have:

[0123] (x i -x k ) 2 +(y i -y k ) 2 =(R i1 +R1) 2 =R i 2 ;(x k -x1) 2 +(y k -y1) 2 =d1 2 ;

[0124] have:

[0125] 2R i1 R1+R i1 2 =x i 2 +y i 2 -x1 2 -x1 2 -2x k (x i -x1)-2y k (y i -y1);

[0126] Let x i 2 +y i 2 =K i 2 Then we have:

[0127] 2x k (x i -x1)+2y k (y i -y1)+2Ri1 R1 = K i 2 -K1 2 -R i1 2 ;

[0128] make

[0129] The matrix representation is AX k =B.

[0130] In practice, there are errors in the distance measurement process, so the actual test result is AX. k +N = B; where N represents the error vector. The sum of squared errors |N| 2 =N T N = (AX k -B) T (AX k -B), let F(X) k ) = (AX k -B) T (AX k -B), to minimize the error N (|N| 2 Minimum), F(X) j ) for X j Taking the derivative and setting it to 0, we have: The least squares estimate is obtained: X k =(A T A) -1 A T B.

[0131] 2. The Kalman filter algorithm is used to calculate the location of the onboard UWB base station and the train speed in real time. Figure 8 The flowchart of the Kalman filter algorithm provided in this application includes: state prediction, error prediction, calculation of Kalman gain, calculation of state, and calculation of error.

[0132] Because the train's acceleration is relatively large during EB (emergency braking) (the maximum acceleration caused by EB and the acceleration caused by the gradient can reach 120 cm / s²), 2 To improve train positioning accuracy, it is necessary to consider the impact of acceleration on the calculation of the onboard UWB base station location and the uncertainty error of train position. This application introduces a Kalman filter algorithm, which considers the impact of acceleration on train position calculation, thus improving train positioning accuracy. Introducing this algorithm also eliminates the dependency between the ground equipment operating cycle and the UWB ranging cycle, while simultaneously enabling real-time calculation of train speed. Furthermore, the algorithm allows for real-time iterative calculation of the train position covariance, improving the accuracy of variance calculation.

[0133] Suppose the vector of the target state at time k that needs to be calculated is... p k p represents the location of a vehicle-mounted UWB base station at time k. kx p represents the x-axis coordinate of the UWB base station at time k in a two-dimensional Cartesian coordinate system. ky This represents the y-axis coordinate of the UWB base station at time k in a two-dimensional Cartesian coordinate system. k v represents the speed of the train at time k. kx Let v represent the x-axis velocity component of the train at time k in a two-dimensional Cartesian coordinate system. ky This represents the y-axis velocity component of the train at time k in a two-dimensional Cartesian coordinate system.

[0134] Kalman filter state prediction process:

[0135] 1) The predicted state equation is Where F k The state transition matrix represents the state vector. Let X represent the predicted state at time k. (k-1) This represents the state at time k-1; Where T is the UWB ranging period. k Let a be the acceleration of the train at time k. k (a kx and a ky a k (components on the x-axis and y-axis)

[0136] 2) The prediction covariance equation is: in To predict the covariance matrix, Q k Let σ be the process noise covariance matrix. k 2 For the variance of acceleration, σ kx 2 Let σ be the variance of the acceleration in the x-direction. ky 2 Let be the variance of the acceleration in the y-direction.

[0137]

[0138] Kalman filter state update process:

[0139] 3) The Kalman gain calculation equation is: Where K is the Kalman gain, and R... k Here is the noise covariance matrix of the location of the vehicle-mounted UWB base station, calculated using the least squares method. The noise standard deviation σ x and σ yH was determined through experimental calibration and on-site testing and debugging. k For the observation matrix,

[0140] 4) The state equation is Where X k Let z be the state at time k. k Let k be the location of the UWB base station calculated using the least squares method. Where p zx and p zy These are the x and y components of the location of the vehicle-mounted UWB base station at time k, calculated using the least squares method.

[0141] 5) Covariance equation Where P k Let P be the covariance matrix at time k. k The initial value is

[0142] 3. Determination of the final target state. In the rail transit signaling system, the rail transit signaling equipment periodically receives UWB periodic ranging data. Usually, the processing cycle of the ground signaling equipment is much longer than the UWB ranging cycle. Therefore, one rail transit signaling equipment cycle needs to calculate the position of the on-board UWB base station for multiple UWB ranging cycles. The position of the on-board UWB base station and the train speed at each moment are updated by iterative calculation and Kalman filtering.

[0143] Assume the vector of the target state at the last UWB ranging time m is covariance matrix The final value of the target state

[0144] 4. Determination of train safety envelope and train speed.

[0145] 1) Determining train speed. Train speed

[0146] 2) According to X e Calculate its position Y on the track. e Calculate X based on the turnout direction. e The vertical projection position Y of the position on the track e . Figure 9 This is a schematic diagram illustrating the estimated location of the vehicle-mounted UWB base station on the track provided in this application.

[0147] 3) Vehicle end position P e The determination of P. e =Y e +D, where D is the distance vector from the vehicle-mounted UWB base station to the vehicle.

[0148] 4) Uncertainty error σ at the vehicle end positione The determination of σ. e =6σ+σ v +σ f , σ f To minimize uncertainty error, based on engineering tests (known errors include those caused by algorithm simplification, model simplification, and acceleration, etc.), noise filtering of the ranging values ​​ensures that the position calculation has no significant deviation. σ v =v e T represents the estimation error caused by processing delay. The value of σ is calculated from the covariance matrix during the Kalman filter calculation process, and the variance value calculation is more accurate.

[0149] 5) Determining the safety envelope of the train. Maximum safe leading edge: estimated position of the train head + σ e Minimum safe front end: Estimated position of the vehicle front - σ e Maximum safe rear end: estimated rear position + σ e Minimum safe rear end: Estimated rear end position - σ e . Figure 10 This is a schematic diagram of the train position envelope provided in this application.

[0150] 5. Handling scenarios where location fails.

[0151] 1) When the vehicle-mounted UWB base station positioning cannot be calculated by the least squares method for multiple consecutive cycles (the number of consecutive cycles m is greater than or equal to the maximum value M), the train is judged to have lost positioning and lost train speed;

[0152] 2) When the location of the vehicle-mounted UWB base station cannot be calculated by the least squares method for multiple consecutive cycles (the number of consecutive cycles m is less than the maximum value M), then the final value of the target state is...

[0153] Estimation error due to processing delay Where a m It is the sum of the absolute values ​​of the train's emergency braking acceleration and the acceleration caused by the maximum gradient of the track.

[0154] The handling of positioning failure scenarios is a system availability optimization based on ensuring the safe and controllable position of the train. It aims to ensure that the railway signaling system can still operate normally in the event of occasional short-term loss of train positioning in extreme cases.

[0155] This application uses a combination of Time-of-Flight (TOF) and Time-of-Operation (TDOA) fusion algorithms with the least squares method to locate a vehicle-mounted UWB base station at a specific moment. By using the ranging information from ground-based UWB base stations and the vehicle-mounted UWB base station, the difference between the ranging measurements of the ground-based UWB base station and the vehicle-mounted UWB base station is calculated. Then, the TDOA algorithm and the least squares method are used to estimate the position of the vehicle-mounted UWB base station. This method solves the problem of fixed ranging bias in the TOF algorithm affecting positioning accuracy, thus improving positioning accuracy and stability. The Kalman filter algorithm is used to more accurately calculate the train's position and speed in real time. After multi-point UWB positioning, the Kalman filter algorithm is used to recursively predict the train's position and speed.

[0156] The prediction equations are calculated based on the train kinematics formulas:

[0157]

[0158] v kx =v (k-1)x +a (k-1)x T;

[0159] v ky =v (k-1)y +a (k-1)y T;

[0160] The prediction equation matrix is ​​expressed as follows: in, Where u k The calculation formula is: a kx The calculation formula is: a ky The calculation formula is: B k for: u k for:

[0161] Process noise covariance middle,

[0162] Optionally, when performing Kalman filter algorithm calculations, if the target positioning and velocity measurement accuracy requirements are not high, the process noise can be set to 0; to simplify the calculation, the standard deviation of the process noise and the observation noise can be used without distinguishing between the x and y directions, using the same value for both directions, but this will reduce the target positioning and velocity measurement accuracy.

[0163] Figure 11 This is a schematic diagram of the train positioning device based on the Kalman filter algorithm provided in this application. The device includes:

[0164] The first determining module 11 is configured to, for at least three terrestrial UWB base stations, acquire a first distance between each terrestrial UWB base station and a vehicle-mounted UWB base station measured at a preset period; determine a first projection distance of each first distance on the track plane based on each first distance acquired in the current period and a pre-saved height difference between each terrestrial UWB base station and the vehicle-mounted UWB base station in the direction perpendicular to the track plane; and determine the first position information of the vehicle-mounted UWB base station in the current period based on the position information of each terrestrial UWB base station and the first projection distance.

[0165] Prediction module 12 is used to predict the third location information of the vehicle-mounted UWB base station in the current period based on the second location information of the vehicle-mounted UWB base station determined in the previous period and the prediction state equation in the Kalman filter algorithm.

[0166] The second determining module 13 is used to determine the fourth location information of the vehicle-mounted UWB base station in the current period based on the first location information and the third location information of the vehicle-mounted UWB base station and the state equation in the Kalman filter algorithm.

[0167] The third determining module 14 is used to determine the target position information of the train in the current period based on the fourth position information of the vehicle-mounted UWB base station and the relative positional relationship between the vehicle-mounted UWB base station and the train.

[0168] The prediction module 12 is specifically used to predict the acceleration of the train in the current period based on the speed of the train determined in the previous period and the preset period; determine the control vector matrix based on the acceleration and the preset period; and predict the third location information of the on-board UWB base station in the current period based on the prediction state equation in the Kalman filter algorithm, according to the state transition matrix of the pre-saved state vector, the second location information of the on-board UWB base station determined in the previous period, and the control vector matrix.

[0169] Prediction module 12 is specifically used to determine the train speed based on the period preceding the current period and the preset period, according to the formula... Predict the acceleration of the train in the current cycle; T is the preset cycle, a k v is the acceleration of the train in the current period k. (k-1) The speed of the train determined by the period k-1, v (k-2) The speed of the train determined by the period k-2;

[0170] The control vector matrix is ​​determined based on the acceleration and the preset period. akx Let a be the acceleration component along the track direction in the track plane. ky This represents the acceleration component in the orbital plane along the direction perpendicular to the orbit.

[0171] Based on the state transition matrix of the pre-saved state vector The second location information X of the vehicle-mounted UWB base station determined in the previous cycle (k-1) and the control vector matrix B k u k Based on the prediction state equation in the Kalman filter algorithm, the third location information of the vehicle-mounted UWB base station in the current period is predicted as follows:

[0172] The second determining module 13 is specifically used to: determine the acceleration variance based on the train's predicted acceleration in the current cycle, the train's predicted acceleration in the previous cycle, and the preset cycle; determine the process noise covariance matrix based on the acceleration variance and the preset cycle; determine the prediction covariance matrix for the current cycle based on the covariance matrix determined in the previous cycle, the state transition matrix of the state vector, and the process noise covariance matrix; determine the Kalman gain based on the Kalman gain calculation equation in the Kalman filtering algorithm based on the prediction covariance matrix for the current cycle, the pre-saved observation matrix, and the pre-determined noise covariance matrix; and determine the fourth location information of the onboard UWB base station for the current cycle based on the state equation in the Kalman filtering algorithm based on the first location information, the third location information, the Kalman gain, and the observation matrix.

[0173] The second determining module 13 is specifically used to determine the acceleration a predicted by the train in the current cycle. k The train's predicted acceleration a in the previous cycle k-1 And the preset period T, determine the acceleration variance as

[0174] According to the acceleration variance The process noise covariance matrix is ​​determined by the preset period T. Where, σ kx 2 Let σ be the variance component of the acceleration along the track direction in the track plane. ky 2 The variance component of acceleration along the direction perpendicular to the track in the track plane;

[0175] Based on the covariance matrix P determined in the previous period (k-1) The state transition matrix F of the state vector kand the process noise covariance matrix Q k The prediction covariance matrix for the current period is determined as follows:

[0176] Based on the predicted covariance matrix of the current period Pre-saved observation matrix and a predetermined noise covariance matrix Based on the Kalman gain calculation equation in the Kalman filter algorithm, the Kalman gain is determined as follows: Where, σ x and σ y These are the pre-calibrated noise standard deviations;

[0177] According to the first location information of the vehicle-mounted UWB base station The third location information The Kalman gain K and the observation matrix H k Based on the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station in the current period is determined as follows: Where, p zx p is the first position component along the track direction in the track plane. zy It is the second position component along the track direction in the track plane.

[0178] The device further includes:

[0179] The fourth determining module 15 is used to determine the prediction covariance matrix of the current period. The Kalman gain K and the observation matrix H k Based on the covariance equation in the Kalman filter algorithm, the covariance matrix of the current period is determined as follows: in, The initial value of the covariance matrix is

[0180] The second determining module 13 is specifically used to determine the location information of the vehicle-mounted UWB base station. The third location information The Kalman gain K and the observation matrix H k Based on the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station and the speed of the train in the current period are determined as follows: Among them, v kx v is the velocity component along the track direction in the track plane of the train determined by the current period k. ky The velocity component of the train in the track plane along the direction perpendicular to the track, determined for the current period k.

[0181] The third determining module 14 is further configured to determine a first error value based on the position parameters in the covariance matrix of the current period; determine a second error value based on the speed of the train in the current period and the preset period; determine an envelope adjustment value based on the first error value, the second error value and the preset third error value; and determine the envelope position information of the train based on the target position information of the train in the current period and the envelope adjustment value.

[0182] The third determining module 14 is specifically used to determine the covariance matrix P of the current period. k The position parameter σ in px 2 and σ py 2 Determine the first error value The train's speed v according to the current cycle e And the preset period T, determine the second error value σv=v e T; where, Based on the first error value σ, the second error value σv, and the preset third error value σf, the envelope adjustment value is determined to be σe = 6σ + σv + σf.

[0183] The device further includes:

[0184] The fifth determining module 16 is used to determine the first location information of the vehicle-mounted UWB base station in the current period according to the formula if the first location information of the vehicle-mounted UWB base station in the current period cannot be determined. Determine the fourth location information of the vehicle-mounted UWB base station in the current period and the speed of the train in the current period; wherein, v mx v is the velocity component along the track direction in the track plane of the train, determined by the previous period m. my The velocity component of the train in the track plane perpendicular to the track direction, determined by the previous period m; a mx a is the acceleration component along the track direction in the track plane of the train, determined by the previous period m; my The acceleration component of the train in the track plane perpendicular to the track direction, as determined by the previous period m;

[0185] The second error value is determined to be in, a m Let m be the acceleration of the train during the preceding period.

[0186] This application also provides an electronic device, such as Figure 12As shown, it includes: processor 21, communication interface 22, memory 23 and communication bus 24, wherein processor 21, communication interface 22 and memory 23 communicate with each other through communication bus 24;

[0187] The memory 23 stores a computer program, which, when executed by the processor 21, causes the processor 21 to perform any of the above method steps.

[0188] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0189] Communication interface 22 is used for communication between the above-mentioned electronic device and other devices.

[0190] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0191] The processors mentioned above can be general-purpose processors, including central processing units, network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0192] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform any of the above method steps.

[0193] This application provides a computer program product, which includes an executable program that, when executed by a processor, implements the method described herein.

[0194] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0195] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A train positioning method based on the Kalman filter algorithm, characterized in that, The method includes: For at least three terrestrial UWB base stations, a first distance between each terrestrial UWB base station and the vehicle-mounted UWB base station is obtained according to a preset period. Based on each first distance obtained in the current period and the pre-saved height difference between each terrestrial UWB base station and the vehicle-mounted UWB base station in the vertical track plane direction, each first distance is determined as a first projection distance on the track plane. Based on the position information of each terrestrial UWB base station and the first projection distance, the first position information of the vehicle-mounted UWB base station in the current period is determined. Based on the second location information of the vehicle-mounted UWB base station determined in the previous cycle, the third location information of the vehicle-mounted UWB base station in the current cycle is predicted based on the prediction state equation in the Kalman filter algorithm. Based on the first location information and the third location information of the vehicle-mounted UWB base station, and using the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station in the current period is determined. Based on the fourth location information of the vehicle-mounted UWB base station and the relative positional relationship between the vehicle-mounted UWB base station and the train, the target location information of the train in the current period is determined.

2. The method as described in claim 1, characterized in that, The step of predicting the third location information of the vehicle-mounted UWB base station in the current period based on the second location information of the vehicle-mounted UWB base station determined in the previous period and the prediction state equation in the Kalman filter algorithm includes: Based on the train speed determined from the previous period and the preset period, predict the train's acceleration in the current period; determine the control vector matrix based on the acceleration and the preset period; Based on the pre-saved state vector state transition matrix, the second location information of the vehicle-mounted UWB base station determined in the previous period, and the control vector matrix, the third location information of the vehicle-mounted UWB base station in the current period is predicted based on the prediction state equation in the Kalman filter algorithm.

3. The method as described in claim 2, characterized in that, Based on the train speed determined according to the previous cycle and the preset cycle, according to the formula Predict the acceleration of the train in the current cycle; T is the preset cycle, a k v is the acceleration of the train in the current period k. (k-1) The speed of the train determined by the period k-1, v (k-2) The speed of the train determined by the period k-2; The control vector matrix is ​​determined based on the acceleration and the preset period. a kx Let a be the acceleration component along the track direction in the track plane. ky This represents the acceleration component in the orbital plane along the direction perpendicular to the orbit. Based on the state transition matrix of the pre-saved state vector The second location information X of the vehicle-mounted UWB base station determined in the previous cycle (k-1) and the control vector matrix B k u k Based on the prediction state equation in the Kalman filter algorithm, the third location information of the vehicle-mounted UWB base station in the current period is predicted as follows:

4. The method as described in claim 2, characterized in that, Based on the first and third location information of the vehicle-mounted UWB base station, and using the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station for the current period is determined as follows: Based on the train's predicted acceleration in the current cycle, the train's predicted acceleration in the previous cycle, and the preset cycle, determine the acceleration variance; based on the acceleration variance and the preset cycle, determine the process noise covariance matrix; The prediction covariance matrix for the current period is determined based on the covariance matrix determined in the previous period, the state transition matrix of the state vector, and the process noise covariance matrix. Based on the predicted covariance matrix of the current period, the pre-saved observation matrix, and the pre-determined noise covariance matrix, the Kalman gain is determined according to the Kalman gain calculation equation in the Kalman filtering algorithm. Based on the first location information, the third location information, the Kalman gain, and the observation matrix of the vehicle-mounted UWB base station, the fourth location information of the vehicle-mounted UWB base station for the current period is determined according to the state equation in the Kalman filtering algorithm.

5. The method as described in claim 4, characterized in that, Based on the predicted acceleration a of the train in the current cycle k The train's predicted acceleration a in the previous cycle k-1 And the preset period T, determine the acceleration variance as According to the acceleration variance The process noise covariance matrix is ​​determined by the preset period T. Where, σ kx 2 Let σ be the variance component of the acceleration along the track direction in the track plane. ky 2 The variance component of acceleration along the direction perpendicular to the track in the track plane; Based on the covariance matrix P determined in the previous period (k-1) The state transition matrix F of the state vector k and the process noise covariance matrix Q k The prediction covariance matrix for the current period is determined as follows: Based on the predicted covariance matrix of the current period Pre-saved observation matrix and a predetermined noise covariance matrix Based on the Kalman gain calculation equation in the Kalman filter algorithm, the Kalman gain is determined as follows: Where, σ x and σ y These are the pre-calibrated noise standard deviations; According to the first location information of the vehicle-mounted UWB base station The third location information The Kalman gain K and the observation matrix H k Based on the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station in the current period is determined as follows: Where, p zx p is the first position component along the track direction in the track plane. zy It is the second position component along the track direction in the track plane.

6. The method as described in claim 4, characterized in that, The method further includes: Based on the predicted covariance matrix of the current period The Kalman gain K and the observation matrix H k Based on the covariance equation in the Kalman filter algorithm, the covariance matrix of the current period is determined as follows: in, The initial value of the covariance matrix is 7. The method as described in claim 6, characterized in that, Based on the first location information, the third location information, the Kalman gain, and the observation matrix of the vehicle-mounted UWB base station, and using the state equation in the Kalman filtering algorithm, the fourth location information of the vehicle-mounted UWB base station for the current period is determined as follows: According to the first location information of the vehicle-mounted UWB base station The third location information The Kalman gain K and the observation matrix H k Based on the state equation in the Kalman filter algorithm, the fourth location information of the vehicle-mounted UWB base station and the speed of the train in the current period are determined as follows: Among them, v kx v is the velocity component along the track direction in the track plane of the train determined by the current period k. ky The velocity component of the train in the track plane along the direction perpendicular to the track, determined for the current period k.

8. The method as described in claim 7, characterized in that, The method further includes: A first error value is determined based on the position parameters in the covariance matrix of the current period; a second error value is determined based on the speed of the train in the current period and the preset period; an envelope adjustment value is determined based on the first error value, the second error value, and the preset third error value; and the envelope position information of the train is determined based on the target position information of the train in the current period and the envelope adjustment value.

9. The method as described in claim 8, characterized in that, Based on the covariance matrix P of the current period k The position parameter σ in px 2 and σ py 2 Determine the first error value The speed v of the train according to the current cycle e And the preset period T, determine the second error value σv=v e T; among which, Based on the first error value σ, the second error value σv, and the preset third error value σf, the envelope adjustment value is determined to be σe = 6σ + σv + σf.

10. The method as described in claim 9, characterized in that, The method further includes: If the first location information of the vehicle-mounted UWB base station in the current period cannot be determined, according to the formula Determine the fourth location information of the vehicle-mounted UWB base station in the current period and the speed of the train in the current period; wherein, v mx v is the velocity component along the track direction in the track plane of the train, determined by the previous period m. my The velocity component of the train in the track plane perpendicular to the track direction, determined by the previous period m; a mx a is the acceleration component along the track direction in the track plane of the train, determined by the previous period m; my The acceleration component of the train in the track plane perpendicular to the track direction, as determined by the previous period m; The second error value is determined to be in, a m Let m be the acceleration of the train during the preceding period.

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