CN114537477A - Train positioning and tracking method based on TDOA - Google Patents

Train positioning and tracking method based on TDOA Download PDF

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CN114537477A
CN114537477A CN202210196284.0A CN202210196284A CN114537477A CN 114537477 A CN114537477 A CN 114537477A CN 202210196284 A CN202210196284 A CN 202210196284A CN 114537477 A CN114537477 A CN 114537477A
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base station
train
tracking
terminal
positioning
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CN114537477B (en
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吴仕勋
李敏
陈瑜
徐凯
张淼
黄大荣
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Shenzhen Xunzu Technology Co ltd
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Chongqing Jiaotong University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L25/00—Recording or indicating positions or identities of vehicles or trains or setting of track apparatus
    • B61L25/02—Indicating or recording positions or identities of vehicles or trains
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a train positioning and tracking method based on TDOA, which is characterized by comprising the following steps: the head terminal acquires downlink signals of positioning signals of three base stations governed by a k-time tracking base station group in real time, the tail terminal acquires downlink signals of positioning signals of three base stations governed by the k-time tracking base station group in real time, and TDOA values corresponding to the head terminal and the tail terminal at the k time are respectively calculated according to the downlink signals acquired by the head terminal and the downlink signals acquired by the tail terminal; then, according to the obtained TDOA value, a UKF algorithm is adopted to obtain a positioning value (a) of the position of the head terminal of the train at the moment k
Figure DEST_PATH_IMAGE002
，
Figure DEST_PATH_IMAGE004
) The head terminal of the train at the time kLocation value of the location (
Figure 607281DEST_PATH_IMAGE002
，
Figure 993263DEST_PATH_IMAGE004
) As a train positioning value. The method of the invention is adopted to carry out positioning tracking on the train, and the positioning precision is greatly improved.

Description

Train positioning and tracking method based on TDOA
Technical Field
The invention relates to the technical field of transportation, in particular to a train positioning and tracking method based on TDOA.
Background
The railway is an important infrastructure for economic development in China, a train operation control system is a core system for controlling safe operation of a train, train positioning is one of key technologies in the train control system, the train positioning is a foundation for guaranteeing the safe operation of the train, and train position information is provided for an Automatic Train Protection (ATP) system in the train control system. The train control system can timely make corresponding decisions by feeding back information such as the speed and the position of the train in real time through train positioning, reliably and safely indicate the train to make proper and correct deceleration operation, avoid safety accidents and ensure the safe running of the train, thereby effectively controlling the train to run and improving the transportation efficiency.
The most common train positioning method at present is a positioning mode based on GPS/INS. However, in the train positioning process of the GNSS-based train positioning method, the navigation satellite signal is easily blocked by shielding objects such as buildings, mountains, trees, and the like, and the satellite signal is deteriorated due to speed measurement errors, multipath reflection errors, clock errors, atmospheric layer delay, and instrument delay, for example, when a train enters a lot of shielding areas around a station, a mountain area, and the like, the positioning accuracy is reduced. In particular, when a train enters a mountainous area multi-tunnel environment, positioning cannot be performed without GNSS signals. Inertial navigation systems are capable of providing short-term, high-precision positioning results, but suffer from the problem that the resulting errors accumulate over time. The combined positioning system based on the GPS/INS has overlarge dependence on GPS signals, when satellite signals are interfered, INS errors are continuously increased along with time, and the precision of the train positioning system is greatly reduced.
In the prior art, there is also a scheme of positioning a train by using a wireless positioning technology such as TDOA (Time Difference of Arrival), which is a method of positioning by using a Time Difference, specifically, by comparing absolute Time differences of signals of respective base stations arriving at a mobile terminal and converting the Time differences into distance differences, a hyperbola with a base station as a focus and a distance Difference as a major axis can be formed, and an intersection point of the hyperbola is a position of the signal. However, the measurement accuracy of the method is affected by various factors, such as time synchronization errors among base stations, transmission power errors among base stations, and the like, and the train positioning accuracy measured by the method needs to be further improved.
Disclosure of Invention
Aiming at the problems of the background art, the invention provides a train positioning and tracking method based on TDOA, which aims to solve the problem of low positioning and tracking precision of trains in the prior art.
In order to achieve the purpose of the invention, the invention provides a train positioning and tracking method based on TDOA, which has the innovation points that:
setting a train running line to be composed of a plurality of tracking road sections, wherein each tracking road section is provided with three base stations for tracking a train, and recording three base stations corresponding to a single tracking road section as a tracking base station group corresponding to the tracking road section; respectively arranging a positioning signal receiving terminal device at the head and the tail of the train, recording the positioning signal receiving terminal device arranged at the head of the train as a head terminal, and recording the positioning signal receiving terminal device arranged at the tail of the train as a tail terminal; recording a tracking base station group corresponding to a tracking road section where the train is located at the moment k as an x tracking base station group;
the train positioning and tracking method comprises the following steps: the head terminal acquires downlink signals of positioning signals of three base stations governed by the x tracking base station group at the moment k in real time, the tail terminal acquires downlink signals of positioning signals of three base stations governed by the x tracking base station group at the moment k in real time, and the head terminal at the moment k is respectively solved according to the downlink signals acquired by the head terminal and the downlink signals acquired by the tail terminalTDOA values corresponding to the end terminal and the tail terminal respectively; then, according to the obtained TDOA value, a UKF algorithm is adopted to obtain a positioning value (x) of the position of the head terminal of the train at the moment kk，yk) The location value (x) of the position of the head terminal of the train at the time kk，yk) As a positioning value of the train;
wherein, the state equation related to the UKF algorithm is as follows: xk＝f(Xk-1)+Wk-1(ii) a The UKF algorithm relates to the following observation equations: zk＝h(Xk)+Vk(ii) a Wherein the content of the first and second substances,
Xka system state vector at time k, defined as:
Figure BDA0003525907050000021
wherein xkAnd ykRespectively the abscissa and ordinate of the position of the train head terminal at the moment k, vkAnd alphakRespectively the speed and the angle of the train at the moment k;
f(Xk-1) Is a nonlinear equation of state function defined as:
Figure BDA0003525907050000022
wherein, Xk-1Is the system state vector of the train at the moment k-1,
Figure BDA0003525907050000023
wherein xk-1And yk-1Respectively the abscissa and ordinate of the position of the train head terminal at the moment k-1, vk-1And alphak-1Respectively the speed and the angle of the train at the moment of k-1; delta t is the time interval between the moment k-1 and the moment k;
Wk-1the system noise vector at the k-1 moment is taken as the reference;
Zkthe system measurement vector at time k is defined as:
Figure BDA0003525907050000024
wherein d is2,1＝d2-d1，d3,1＝d3-d1，d′2,1＝d′2-d′1，d′3,1＝d′3-d′1D is said1、d2And d3Respectively the distances from the train head terminal to a first base station, a second base station and a third base station governed by the x tracking base station group, and d'1、d′2And d'3Respectively obtaining the distances from the tail terminal of the train to a first base station, a second base station and a third base station which are governed by the x tracking base station group;
h(Xk) Is a nonlinear observation equation function defined as:
Figure BDA0003525907050000031
wherein, x'kAnd y'kRespectively an abscissa and an ordinate of the position of the train tail terminal at the moment k; x is the number of1、x2And x3Respectively tracking the horizontal coordinates of the positions of a first base station, a second base station and a third base station which are governed by the base station for the x; y is1、y2And y3Respectively tracking the vertical coordinates of the positions of a first base station, a second base station and a third base station which are governed by the base station for the x; wherein, x'k＝xk-Lcosα，y′k＝yk-Lsin α, said L being the distance from said head terminal to said tail terminal;
Vkis the measurement noise vector at time k.
And as optimization, the communication among the base station, the head terminal and the tail terminal adopts a 5G-R network system.
As optimization, the tracking section comprises an open section, and three base stations governed by a single tracking base station group of the open section are arranged in the following manner: the first base station is arranged on the right side of the track, the second base station and the third base station are arranged on the left side of the track, the vertical distances from the second base station and the third base station to the central axis of the track are equal, and the linear distances from the second base station and the third base station to the first base station are equal.
As optimization, the method is characterized in that: the tracking road section comprises a platform road section, and the platform road section realizes 5G-R network coverage in a pico-base station mode.
And as optimization, the tracking road section comprises a tunnel road section, and the tunnel road section realizes 5G-R network coverage by laying a leaky coaxial cable.
The principle of the invention is as follows:
in the prior art, although the TDOA technology can be used to obtain the location information of the train, the observed data using the above technology includes the influence of measurement noise and interference, and the measured data has a large deviation from the real position of the train. Data filtering is a data processing technique for solving the above-mentioned problems, which can restore real data by removing noise, for example, can estimate coordinate position and velocity of an object from a limited set of observation sequences containing noise. The data filtering technology comprises KF (Kalman filtering), EKF (extended Kalman filtering), UKF (unscented Kalman filtering), PF (particle filtering) and the like, because the problem of train positioning and tracking to be solved by the invention is a nonlinear system problem, but KF can only solve the linear system problem, EKF, UKF and PF can solve the nonlinear system problem, but EKF and PF have complex calculation, low calculation speed and high investment cost, the inventor finds that UKF is more suitable for the nonlinear system because of high calculation precision and high calculation speed block for the nonlinear system of train positioning and tracking; on the other hand, in the prior art, usually, the train is taken as a whole, and only one positioning signal receiving terminal is arranged on the train to obtain the TDOA value of the train, although the data noise can be removed by the UKF algorithm to improve the measurement accuracy, the inventor finds that the accuracy of the train positioning tracking technology further improves the space by researching the train operation characteristics and the UKF algorithm: in fact, in the algorithm of the UKF, if more observed values can be introduced, the train positioning value estimated by the UKF algorithm after fusing the observed values and the estimated values can be closer to the true value, that is, the train positioning and tracking accuracy can be further improved. The inventionIn the method, a positioning signal receiving terminal is respectively arranged at the head and the tail of a train, the positioning signal receiving terminals at the head and the tail of the train simultaneously receive downlink information of three base stations governed by the same tracking base station group at the same time, respective TDOA values are obtained, and position information (x 'of a tail terminal can be obtained through a mathematical model, the distance between the two positioning signal receiving terminals and the angle of the train'k，y′k) And location information (x) of the head terminalk，yk) And converting, namely taking the position information of one positioning signal receiving terminal as the positioning information of the whole train, so that two groups of observation data can be obtained at the moment for the position of the positioning signal receiving terminal, and the precision of the train positioning tracking estimation value obtained by filtering and denoising the two groups of observation data and the system estimation data through a UKF algorithm is greatly improved. The invention adopts the position of the head terminal as the positioning position of the whole vehicle, and can also adopt the position of the tail terminal as the positioning position of the whole vehicle.
Therefore, the invention has the following beneficial effects: the method of the invention is adopted to position and track the train, and the positioning precision can be greatly improved.
Drawings
The drawings of the present invention are described below.
FIG. 1 is a schematic structural diagram of the present invention;
FIG. 2 is a schematic diagram of the arrangement structure of base stations in an open road section;
FIG. 3 is a schematic diagram of the layout of the base stations in the station section;
fig. 4 is a schematic diagram of the arrangement structure of the base stations in the tunnel section.
Wherein, 1, tracking a base station group; 2. a head terminal; 3. a tail terminal; 11. a first base station; 12. a second base station; 13. and a third base station.
Detailed Description
The present invention will be further described with reference to the following examples.
As shown in fig. 1, a train running line is composed of a plurality of tracking sections, each tracking section is provided with three base stations for tracking a train, and the three base stations corresponding to a single tracking section are marked as a tracking base station group 1 corresponding to the tracking section; respectively arranging a positioning signal receiving terminal device at the head and the tail of the train, recording the positioning signal receiving terminal device arranged at the head of the train as a head terminal 2, and recording the positioning signal receiving terminal device arranged at the tail of the train as a tail terminal 3; recording a tracking base station group 1 corresponding to a tracking road section where the train is located at the moment k as an x tracking base station group 1;
the train positioning and tracking method comprises the following steps: the head terminal 2 acquires downlink signals of positioning signals of three base stations governed by the x tracking base station group 1 at the time k in real time, the tail terminal 3 acquires downlink signals of positioning signals of three base stations governed by the x tracking base station group 1 at the time k in real time, and TDOA values corresponding to the head terminal 2 and the tail terminal 3 at the time k are respectively calculated according to the downlink signals acquired by the head terminal 2 and the downlink signals acquired by the tail terminal 3; then, according to the obtained TDOA value, a UKF algorithm is adopted to obtain a positioning value (x) of the position of the head terminal 2 of the train at the moment kk，yk) The location value (x) of the position of the head terminal 2 of the train at the time k is calculatedk，yk) As a positioning value of the train;
wherein, the state equation related to the UKF algorithm is as follows: xk＝f(Xk-1)+Wk-1(ii) a The UKF algorithm relates to the following observation equations: zk＝h(Xk)+Vk(ii) a Wherein the content of the first and second substances,
Xka system state vector at time k, defined as:
Figure BDA0003525907050000051
wherein xkAnd ykRespectively an abscissa and an ordinate, v, of the position of the train head terminal 2 at time kkAnd alphakThe speed and the angle of the train at the moment k are respectively, wherein the speed is the speed along the running direction of the train, and the angle refers to the degree of an included angle between the running direction of the train and the east direction of a coordinate axis;
f(Xk-1) Is non-linearAn equation of state function defined as:
Figure BDA0003525907050000052
wherein, Xk-1Is the system state vector of the train at the moment k-1,
Figure BDA0003525907050000053
wherein xk-1And yk-1Respectively an abscissa and an ordinate, v, of the position of the train head terminal 2 at the time k-1k-1And alphak-1Respectively the speed and the angle of the train at the moment of k-1; delta t is the time interval between the moment k-1 and the moment k;
Wk-1is the system noise vector at time k-1, Wk-1White Gaussian noise with covariance matrix Qk-1，Wk-1The setting can be carried out according to the acceleration information in the train motion equation;
Zkthe system measurement vector at time k is defined as:
Figure BDA0003525907050000054
wherein d is2,1＝d2-d1，d3,1＝d3-d1，d′2,1＝d′2-d′1，d′3,1＝d′3-d′1D is said1、d2And d3Respectively tracking the distances from the head terminal 2 of the train to a first base station 11, a second base station 12 and a third base station 13 belonging to the base station group 1 of the x, and d'1、d′2And d'3Distances from the train tail terminal 3 to a first base station 11, a second base station 12 and a third base station 13 which are governed by the x tracking base station group 1 are respectively set;
h(Xk) Is a nonlinear observation equation function defined as:
Figure BDA0003525907050000061
wherein, x'kAnd y'kRespectively an abscissa and an ordinate of the position of the train tail terminal 3 at the moment k; x is the number of1、x2And x3Respectively tracking the horizontal coordinates of the positions of a first base station 11, a second base station 12 and a third base station 13 which are governed by the base stations for the x; y is1、y2And y3Respectively tracking the vertical coordinates of the positions of a first base station 11, a second base station 12 and a third base station 13 which are governed by the base stations for x; wherein, x'k＝xk-Lcosα，y′k＝yk-lssin α, said L being the distance of said head terminal 2 to the tail terminal 3;
Vkmeasured noise vector for time k, said VkWhite Gaussian noise with covariance matrix of Rk，VkThe setting can be made according to the measurement accuracy of the TDOA of the 5G-R system.
Besides the state equation and the observation equation, the specific calculation principle and steps of the UKF algorithm are common processing means in the prior art, and related contents can be obtained from related documents in the prior art by a person skilled in the art. In this embodiment, the calculation steps of the UKF algorithm are briefly introduced as follows:
(1) initializing a system state vector estimate X0And its error covariance matrix P0，k＝1,2,…；
(2) Obtaining 2n +1 Sigma sampling points and their corresponding weights, using UT transform, i.e.
Figure BDA0003525907050000062
Figure BDA0003525907050000063
Figure BDA0003525907050000064
Wherein the content of the first and second substances,
Figure BDA0003525907050000065
represents the ith column of the matrix;
Figure BDA0003525907050000066
Figure BDA0003525907050000067
Figure BDA0003525907050000068
in the formula, subscripts m and c are respectively mean value and covariance, and the superscript represents a few sampling points; λ ═ α2(n + κ) -n is a scaling parameter used to reduce the overall prediction error, where α is usually a small positive number, κ is usually 0, and β is usually 2;
(3) the state prediction and covariance at the moment k are obtained through calculation,
Figure BDA0003525907050000071
Figure BDA0003525907050000072
Figure BDA0003525907050000073
(4) according to the predicted value obtained in the step (3)
Figure BDA0003525907050000074
And covariance
Figure BDA0003525907050000075
Step (2) is carried out again to obtain Sigma points again
Figure BDA0003525907050000076
And corresponding weights
Figure BDA0003525907050000077
Figure BDA0003525907050000078
Figure BDA0003525907050000079
Figure BDA00035259070500000710
(5) Sigma spot
Figure BDA00035259070500000711
Substituting the variable value into an observation equation to obtain a predicted observed quantity:
Figure BDA00035259070500000712
wherein:
Figure BDA00035259070500000713
(6) further obtaining a mean and covariance matrix of the observed values:
Figure BDA00035259070500000714
Figure BDA00035259070500000715
Figure BDA00035259070500000716
(7) updating the state and covariance matrix of the system:
Figure BDA00035259070500000717
Figure BDA00035259070500000718
Figure BDA00035259070500000719
in order to make the time delay of signal transmission lower and further improve the accuracy of ranging, a 5G-R (5G for uplink) network system is adopted for communication among the base station, the head terminal 2 and the tail terminal 3. Compared with the past communication system, the MIMO, ultra-dense network, millimeter wave transmission and D2D communication are introduced, so that the communication performance of the system can be further improved, the accuracy and the application range of wireless positioning can be improved, and the network full coverage along the railway can be realized.
As a further optimization, in the invention, the types of the tracking road sections of the running route of the train are divided into an open road section, a platform road section and a tunnel road section, and different network coverage modes are respectively adopted for the three types of road sections, so as to further improve the positioning effect and the precision of the train:
for an open road section, the adopted network coverage mode is that 'DU and CU are respectively provided with + AAU', namely an AAU unit is arranged on the sky surface of a base station along a railway, DU is arranged in a machine room of the base station, and CU is intensively arranged and then is connected with a core network in a return mode. The basic idea of the method is to solve TDOA data from the acquired downlink positioning reference signals by processing three measuring base stations so as to realize the positioning of the receiver. The geometric principle is that two unilateral hyperbolas are determined by using the TDOA values of the arrival time differences between every two three positioning base stations, the intersection point of the curves is the position of the train, but the TDOA may generate two solutions during solving, namely a fuzzy solution. In order to solve the problem of fuzzy solution, base stations need to be reasonably arranged to avoid a positioning fuzzy area. By analyzing the influence of the topological structure of the base station on the TDOA positioning precision, in the three-base-station positioning system, when the triangle formed by the three base stations is an isosceles triangle, the problem of fuzzy solution can be solved. Therefore, in this embodiment, as shown in fig. 2, three base stations covered by a single tracking base station group 1 in an open road are arranged in the following manner: the first base station 11 is arranged on the right side of the track, the second base station 12 and the third base station 13 are arranged on the left side of the track, the vertical distances from the second base station 12 to the central axis of the track are equal to the vertical distances from the third base station 13 to the central axis of the track, and the linear distances from the second base station 12 to the first base station 11 are equal to the linear distances from the third base station 13 to the first base station 11. Therefore, the three base stations are distributed in an isosceles triangle, so that the horizontal geometric accuracy factor of the system is reduced, and the train positioning accuracy is improved.
For the platform road section, because the requirement on the train stopping precision of the platform area is higher, if a network coverage mode of an open area is adopted, the positioning precision cannot meet the requirement due to non-line-of-sight transmission of signals. Therefore, aiming at the platform section, the invention adopts a 5G-R pico-base station mode to realize a network full-coverage scheme. As shown in fig. 3, the 5G-R pico base station is composed of a hub unit (RHub), a pico radio remote unit (pico RRU or pRRU), and a baseband processing unit (BBU). A plurality of pRRUs in the range of the same 5G-R pico base station are organized into the same group and are connected with the same RHUb, and the RHUb is connected with a BBU arranged in a communication machine room through an optical cable. The pRRUs are distributed at two sides of a public area of the platform, are staggered along the track and are in a zigzag shape, so that the network full coverage of the 5G-R signals is ensured.
For the tunnel road section, the invention adopts laying of leaky coaxial cables (leaky cables for short) to carry out network coverage, namely, the 5G-R wireless network coverage is realized by adopting a networking mode of '5 GRUU + POI + leaky cables', and as shown in figure 4, the leaky cables are covered by adopting a mode of single laying on one side of a railway. When in the tunnel, the leaky cable can be hung on the tunnel wall. The leaky cable has the functions of signal transmission and antenna, and can uniformly radiate controlled electromagnetic wave energy along a line and receive the controlled electromagnetic wave energy by controlling the opening of the outer conductor, so that the field intensity is uniformly attenuated without fluctuation, the electromagnetic field blind area is covered, and the aim of smooth mobile communication is fulfilled.
The wireless network coverage distance L (unit: m) of the source access at both ends of the leaky cable can be calculated by the following formula:
Figure BDA0003525907050000091
wherein L is0、L1、L2、L3Respectively the required in-vehicle field intensity, the air coupling loss of the leaky cable, the switching interval and the hectometer attenuation of the common leaky cable, N1、N2Respectively, a jumper terminal loss, a combiner loss, M1、M2、M3、M4Respectively, system margin, width factor, vehicle body dielectric loss and human body loss, PtIs the output power.

Claims (5)

1. A train positioning and tracking method based on TDOA is characterized in that:
setting a train running line to be composed of a plurality of tracking road sections, wherein each tracking road section is provided with three base stations for tracking a train, and recording three base stations corresponding to a single tracking road section as a tracking base station group corresponding to the tracking road section; respectively arranging a positioning signal receiving terminal device at the head and the tail of the train, recording the positioning signal receiving terminal device arranged at the head of the train as a head terminal, and recording the positioning signal receiving terminal device arranged at the tail of the train as a tail terminal; recording a tracking base station group corresponding to a tracking road section where the train is located at the moment k as an x tracking base station group;
the train positioning and tracking method comprises the following steps: the head terminal acquires the x tracking basis at the k moment in real timeDownlink signals of positioning signals of three base stations governed by a station group are acquired by the tail terminal in real time, and downlink signals of the positioning signals of the three base stations governed by the x tracking base station group at the time k are acquired by the tail terminal, and TDOA values corresponding to the head terminal and the tail terminal at the time k are respectively calculated according to the downlink signals acquired by the head terminal and the downlink signals acquired by the tail terminal; then, according to the obtained TDOA value, a UKF algorithm is adopted to obtain a positioning value (x) of the position of the head terminal of the train at the moment kk，yk) The location value (x) of the position of the head terminal of the train at the time kk，yk) As a positioning value of the train;
wherein, the state equation related to the UKF algorithm is as follows: xk＝f(Xk-1)+Wk-1(ii) a The UKF algorithm relates to the following observation equations: zk＝h(Xk)+Vk(ii) a Wherein, the first and the second end of the pipe are connected with each other,
Xka system state vector at time k, defined as:
Figure FDA0003525907040000011
wherein xkAnd ykRespectively an abscissa and an ordinate, v, of the position of the train head terminal at the moment kkAnd alphakRespectively the speed and the angle of the train at the moment k;
f(Xk-1) Is a nonlinear equation of state function defined as:
Figure FDA0003525907040000012
wherein, Xk-1Is the system state vector of the train at the moment k-1,
Figure FDA0003525907040000013
wherein xk-1And yk-1Respectively the abscissa and ordinate of the position of the train head terminal at the moment k-1, vk-1And alphak-1Respectively the speed and the angle of the train at the moment of k-1; Δ t is the time between time k-1 and time kSpacing;
Wk-1the system noise vector at the k-1 moment is taken as the reference;
Zka system measurement vector for time k, defined as:
Figure FDA0003525907040000021
wherein d is2，1＝d2-d1，d3，1＝d3-d1，d′2，1＝d′2-d′1，d′3，1＝d′3-d′1D is said1、d2And d3Respectively the distances from the train head terminal to a first base station, a second base station and a third base station governed by the x tracking base station group, and d'1、d′2And d'3Respectively obtaining the distances from the tail terminal of the train to a first base station, a second base station and a third base station which are governed by the x tracking base station group;
h(Xk) Is a nonlinear observation equation function defined as:
Figure FDA0003525907040000022
wherein, x'kAnd y'kRespectively an abscissa and an ordinate of the position of the train tail terminal at the moment k; x is the number of1、x2And x3Respectively tracking the horizontal coordinates of the positions of a first base station, a second base station and a third base station which are governed by the base station for the x; y is1、y2And y3Respectively tracking the vertical coordinates of the positions of a first base station, a second base station and a third base station which are governed by the base station for the x; wherein, x'k＝xk-Lcosα，y′k＝yk-Lsin α, said L being the distance from said head terminal to said tail terminal;
Vkis the measurement noise vector at time k.
2. The TDOA-based train location tracking method according to claim 1, wherein: and the communication among the base station, the head terminal and the tail terminal adopts a 5G-R network system.
3. The TDOA-based train location tracking method according to claim 2, wherein: the tracking road section comprises an open road section, and three base stations covered by a single tracking base station group of the open road section are arranged in the following mode: the first base station is arranged on the right side of the track, the second base station and the third base station are arranged on the left side of the track, the vertical distances from the second base station and the third base station to the central axis of the track are equal, and the linear distances from the second base station and the third base station to the first base station are equal.
4. A TDOA-based train location tracking method as recited in claim 2, wherein: the tracking road section comprises a platform road section, and the platform road section realizes 5G-R network coverage in a pico-base station mode.
5. A TDOA-based train location tracking method as recited in claim 2, wherein: the tracking road section comprises a tunnel road section, and the tunnel road section realizes 5G-R network coverage by laying a leaky coaxial cable.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114919627A (en) * 2022-06-17 2022-08-19 重庆交通大学 Train positioning and tracking method based on RIS technology
CN116902042A (en) * 2023-09-12 2023-10-20 比亚迪股份有限公司 Positioning method, system and train

Citations (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1994026571A1 (en) * 1993-05-17 1994-11-24 Hughes Aircraft Company Train location and control using spread spectrum radio communications
JPH09119973A (en) * 1995-05-31 1997-05-06 General Electric Co <Ge> Method and system for confirming position of body to be tracked
KR20060004321A (en) * 2004-07-09 2006-01-12 한국철도기술연구원 Position location system of the train on the railway by using the phase difference relation
WO2012065233A1 (en) * 2010-11-19 2012-05-24 Commonwealth Scientific And Industrial Research Organisation Tracking location of mobile devices in a wireless network
CN102923167A (en) * 2012-10-25 2013-02-13 北京交通大学 Train tracking and approaching pre-warning system
DE102012209311A1 (en) * 2012-06-01 2013-12-05 Deutsches Zentrum für Luft- und Raumfahrt e.V. Method for locating position of train in rail topological network, involves multiplying initial weight of hypothesis with all weights of rating obtained by measurements such that hypotheses with high weights is determined
US20140045518A1 (en) * 2010-11-19 2014-02-13 Thuraiappah Sathyan Tracking location of mobile device in a wireless network
CN106143538A (en) * 2016-07-06 2016-11-23 兰州交通大学 A kind of train locating method based on LTE R utilizing double-movement platform
CN108931244A (en) * 2018-07-18 2018-12-04 兰州交通大学 Ins error suppressing method and system based on train kinematic constraint
CN108981692A (en) * 2018-06-14 2018-12-11 兰州晨阳启创信息科技有限公司 It is a kind of based on inertial navigation/visual odometry train locating method and system
CN109229158A (en) * 2018-09-14 2019-01-18 北京全路通信信号研究设计院集团有限公司 Train rapid positioning method and system
CA3106029A1 (en) * 2018-07-17 2020-01-23 Amsted Rail Company, Inc. System and method for building and managing a train consist
CA3074977A1 (en) * 2019-04-12 2020-10-12 Thales Management & Services Deutschland Gmbh A method for safely and autonomously determining a position information of a train on a track
CN112550375A (en) * 2020-11-24 2021-03-26 卡斯柯信号有限公司 Train conductor identification method and system based on satellite positioning
US20210129881A1 (en) * 2019-11-06 2021-05-06 Humatics Corporation Techniques and associated systems and methods for determining train motion characteristics
CN113353122A (en) * 2021-06-25 2021-09-07 重庆交通大学 Control method of virtual coupled high-speed train under brake force fault of tracking train
US20210331727A1 (en) * 2020-04-24 2021-10-28 East China Jiaotong University Method and system for state feedback predictive control of high-speed train based on forecast error
CN114056384A (en) * 2022-01-17 2022-02-18 矿冶科技集团有限公司 Safety early warning system for electric locomotive and early warning method thereof

Patent Citations (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1994026571A1 (en) * 1993-05-17 1994-11-24 Hughes Aircraft Company Train location and control using spread spectrum radio communications
JPH09119973A (en) * 1995-05-31 1997-05-06 General Electric Co <Ge> Method and system for confirming position of body to be tracked
KR20060004321A (en) * 2004-07-09 2006-01-12 한국철도기술연구원 Position location system of the train on the railway by using the phase difference relation
WO2012065233A1 (en) * 2010-11-19 2012-05-24 Commonwealth Scientific And Industrial Research Organisation Tracking location of mobile devices in a wireless network
US20140045518A1 (en) * 2010-11-19 2014-02-13 Thuraiappah Sathyan Tracking location of mobile device in a wireless network
DE102012209311A1 (en) * 2012-06-01 2013-12-05 Deutsches Zentrum für Luft- und Raumfahrt e.V. Method for locating position of train in rail topological network, involves multiplying initial weight of hypothesis with all weights of rating obtained by measurements such that hypotheses with high weights is determined
CN102923167A (en) * 2012-10-25 2013-02-13 北京交通大学 Train tracking and approaching pre-warning system
CN106143538A (en) * 2016-07-06 2016-11-23 兰州交通大学 A kind of train locating method based on LTE R utilizing double-movement platform
CN108981692A (en) * 2018-06-14 2018-12-11 兰州晨阳启创信息科技有限公司 It is a kind of based on inertial navigation/visual odometry train locating method and system
CA3106029A1 (en) * 2018-07-17 2020-01-23 Amsted Rail Company, Inc. System and method for building and managing a train consist
CN108931244A (en) * 2018-07-18 2018-12-04 兰州交通大学 Ins error suppressing method and system based on train kinematic constraint
CN109229158A (en) * 2018-09-14 2019-01-18 北京全路通信信号研究设计院集团有限公司 Train rapid positioning method and system
CA3074977A1 (en) * 2019-04-12 2020-10-12 Thales Management & Services Deutschland Gmbh A method for safely and autonomously determining a position information of a train on a track
US20210129881A1 (en) * 2019-11-06 2021-05-06 Humatics Corporation Techniques and associated systems and methods for determining train motion characteristics
US20210331727A1 (en) * 2020-04-24 2021-10-28 East China Jiaotong University Method and system for state feedback predictive control of high-speed train based on forecast error
CN112550375A (en) * 2020-11-24 2021-03-26 卡斯柯信号有限公司 Train conductor identification method and system based on satellite positioning
CN113353122A (en) * 2021-06-25 2021-09-07 重庆交通大学 Control method of virtual coupled high-speed train under brake force fault of tracking train
CN114056384A (en) * 2022-01-17 2022-02-18 矿冶科技集团有限公司 Safety early warning system for electric locomotive and early warning method thereof

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
SAVAGE, C. O., CRAMER, R. L., & SCHMITT, H. A: "TDOA geolocation with the unscented Kalman filter", 《2006 IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL》, pages 602 - 606 *
WU, S. X., ZHANG, S. J., XU, K., & HUANG, D. R: "A Weighting Localization Algorithm with LOS and One-Bound NLOS Identification in Multipath Environments", 《JOURNAL OF INFORMATION SCIENCE & ENGINEERING》, vol. 35, no. 6, pages 1 - 4 *
WU, S., ZHANG, S., & HUANG, D.: "A TOA-based localization algorithm with simultaneous NLOS mitigation and synchronization error elimination", 《IEEE SENSORS LETTERS》, vol. 3, no. 3, pages 1 - 4, XP011713607, DOI: 10.1109/LSENS.2019.2897924 *
XIA, N., WEI, W., LI, J., & ZHANG, X: "Kalman particle filtering algorithm for symmetric alpha‐stable distribution signals with application to high frequency time difference of arrival geolocation", 《IET SIGNAL PROCESSING》, vol. 10, no. 6, pages 619 - 625, XP006057665, DOI: 10.1049/iet-spr.2014.0279 *
刘翔;宋常建;胡磊;钟子发;: "基于无迹卡尔曼滤波的单站混合定位跟踪算法", 《探测与控制学报》, vol. 34, no. 03, pages 72 - 74 *
徐凯;吴磊;杨飞凤;: "PSO-ICS算法优化的城轨列车自动驾驶***", 《铁道科学与工程学报》, no. 12, pages 202 - 210 *
徐凯;吴磊;赵梅;: "多种群分层联合优化的城轨列车ATO研究", 《铁道学报》, no. 06, pages 93 - 99 *
滕飞;钟子发;: "基于非合作定位模型的改进型粒子滤波跟踪算法", 《探测与控制学报》, vol. 37, no. 04, pages 73 - 75 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114919627A (en) * 2022-06-17 2022-08-19 重庆交通大学 Train positioning and tracking method based on RIS technology
CN114919627B (en) * 2022-06-17 2023-06-09 重庆交通大学 RIS technology-based train positioning tracking method
CN116902042A (en) * 2023-09-12 2023-10-20 比亚迪股份有限公司 Positioning method, system and train
CN116902042B (en) * 2023-09-12 2024-01-09 比亚迪股份有限公司 Positioning method, system and train

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